FR2814894A1 - Industrial site/public place/road traffic intrusion detection system having video scene block transformed/spatial activity determined and previous current image compared with evolving luminosity weighting applied. - Google Patents

Abstract

The intrusion detection method has a video camera surveillance for a video scene and detecting a camera offset or obstruction. The image is transformed (256) in image blocks and spatial activity determined for each block. The current and previous image are calculated providing a comparison base and using a weighting of the evolving luminosity with time, to provide a signal output which is compared to a set detection level.

Description

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METHOD OF DETECTING INTRUSION, IN PARTICULAR BY DETECTING AND / OR OBSTRUCTING A CAMERA, IN A
The invention relates to a method for detecting intrusions in a remote surveillance system using at least one camera.

 Video surveillance is used in various fields of activity, particularly with regard to the surveillance of property and persons, surveillance of public places, surveillance of road traffic.

 The invention is particularly concerned with the surveillance of places in order to detect intrusions by means of a camera, in particular a fixed camera. In the state of the art, it is known to use fixed or mobile cameras connected to a console of control screens that an operator constantly looks at. In practice, operators do not have their attention fixed on screens permanently. In addition, such a system requires having as many screens as the number of cameras.

 A known solution is to display on a small number of screens, sequentially, what films each camera. This solution reduces the number of screens and makes the monitoring of screens less monotonous to increase the vigilance of the person who monitors the screens.

 An improvement consists in doubling each camera of a presence or event detector in order to automatically display the images of a camera whose detector detects a movement on the image. The operator then displays with a control screen what is the situation. The camera can also trigger a recording of images from cameras that have detected the event.

 In order to suppress the detectors associated with each camera, it is possible to use an image processing to detect the movements on the image with respect to a reference image. Conventionally, this is done for fixed cameras that continuously film the same place at a single angle A reference image corresponding to the place to be monitored is digitized and each image filmed is also scanned and compared to the reference image.

 The field of digital image coding has gained considerable importance since the 1980s.

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utilisées, certaines sont dédiées au codage à réduction de débit (normes H261, JPEG, MPEG1, MPEG2, JPEG2000, etc...) et se basent toutes sur le principe de codage par transformation de blocs. Grâce à cette transformation, les algorithmes peuvent calculer des paramètres de corrélation spatiale, de corrélation temporelle, de redondance subjective ou de redondance statistique afin de comprimer fortement les images. Parmi les transformées connues, la transformée en cosinus discrète (par la suite TCD) et la transformée en ondelettes sont les plus utilisées afin de déterminer la corrélation spatiale d'une image.

some are dedicated to rate reduction coding (H261, JPEG, MPEG1, MPEG2, JPEG2000, etc ...) and are all based on block transformation coding. With this transformation, algorithms can compute spatial correlation, time correlation, subjective redundancy, or statistical redundancy parameters to strongly compress images. Of the known transforms, the discrete cosine transform (hereinafter TCD) and the wavelet transform are the most commonly used to determine the spatial correlation of an image.

 These algorithms also use prediction techniques to further reduce the size of compressed images. For these algorithms (such as the MJPEG, MPEG1, MPEG2, JPEG2000 standards), it is possible to predict for an image at a given moment what will have changed in the image at the following moment in order to perform, for example, the motion compensation. These prediction techniques are based on the extraction of the differences between two images by subtracting said images and a motion estimation. In order to maintain an acceptable image quality, the predictor is constantly updated (by compression and decompression techniques). In that case. we speak of image 1 coded intra or instantaneous reference (basic predictor). non-predictive (not predictive) and predictive (temporal prediction) P.sub.B image.

 The detection systems are conventionally based on taking a reference image of the filmed scene and then on comparing the filmed image with said reference image by point-by-point differentiation (that is to say pixel by pixel ) of the filmed image and the reference image to obtain a difference image and finally triggering an alarm if the image contains moving elements, that is to say differences found on several successive images, and displaying the image on a control screen so that the operator visually controls the monitored location.

 Such a system has some defects: - a first defect comes from the fact that the monitored place must be illuminated in a homogeneous way, which is very suitable for places lit by artificial light (eg a room without window ) or indirectly (for example a corridor lit by reflection of light on a wall) On the other hand, if the place to be monitored is illuminated directly by

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la lumière du soleil ou s'il s'agit d'un extérieur, de nombreuses fausses alarmes peuvent se déclencher, réduisant ainsi la vigilance de l'opérateur qui ne va plus pouvoir contrôler les écrans dont les alarmes se déclenchent en permanence : - un deuxième défaut vient également des fausses alarmes qui peuvent être déclenchées par un changement de décor d'un bureau (affiche, plante..), - un troisième inconvénient provient des fausses alarmes dues à un seuil de référence trop bas par rapport à la qualité de l'image ; - un quatrième défaut est que ces techniques ne s'intègrent pas facilement aux procédés de codage d'image à réduction de débit.

sunlight or if it is an outside, many false alarms can be triggered, thus reducing the vigilance of the operator who will no longer be able to control the screens whose alarms are triggered continuously: - a second defect also comes from false alarms that can be triggered by a change of scenery of a desk (poster, plant ..), - a third disadvantage comes from false alarms due to a reference threshold too low compared to the quality of the image ; a fourth defect is that these techniques do not easily integrate with the rate reduction image coding methods.

 The invention proposes a solution that notably makes it possible to significantly reduce false alarms by extracting from the image (or part of the image) a signature allowing detection.

 This signature is a temporal representation of the image, relatively insensitive to changes in brightness or changes of scenery but highly sensitive to intrusions, especially when they are manifested by a change in content of the image, which continues to course of time.

 On the other hand, the invention proposes to develop this signature from block transformation techniques, in particular the discrete cosine transformation TCD. It is thus a question of bringing a strong added value to its techniques without increasing in a big way the volume of computation to be carried out.

 These advantageous characteristics make this system a robust, efficient and effective process.

 According to a first aspect, the invention relates to an intrusion detection method in a remote monitoring system implementing at least one video camera providing a video image which implements for a resulting image obtained by subtracting the values, pixel by pixel a current image and a reference image, a block transform, in at least one characteristic area of the resulting image comprising N blocks, for obtaining for each block Bn, m transformed coefficients Fn, m (i. j), i and j varying from 0 to M, M denoting the maximum number of rows or columns of pixels of a block Bn, m, then calculating a mean spatial activity Az of N blocks of said zone, of the resulting image,

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laquelle constitue un signal numérique de détection d'intrusion qui est comparé avec un seuil prédeterminé de détection d'intrusion pour détecter une intrusion.

which constitutes a digital intrusion detection signal which is compared with a predetermined threshold of intrusion detection to detect an intrusion.

N-I A, =MV] 11-o

L'activité spatiale Asu peut être par exemple calculée à l'aide de la formule (7) donnée plus loin dans la description. The method can implement, for the calculation of the spatial activity Az: - the calculation of the spatial activity Asu of each of the N blocks Bn, m, u varying from 0 to N-1 - the calculation of the activity mean space Az with:

NI A, = MV] 11-o

Asu spatial activity can be calculated for example using the formula (7) given later in the description.

The method can implement a compensation of the variation of the brightness. We then have:

dcu représentant la luminosité moyenne du bloc de rang u. Avantageusement, le procédé met en oeuvre l'application d'une transformée temporelle unidimensionnelle à N'signaux de détection Azo,... AzM-1, calculés pour des images courantes prises à des instants to,... tN'-1, pour obtenir une matrice de coefficients transformés Do, Da... DN'-i, et un calcul d'un signal Ar représentatif d'une évolution temporelle de l'image, avec :

V'- ! Ar= E D u=l

iv'-i Do est proportionnel à- ; L Alli 'N

Le procédé peut être caractérisé en ce qu'il met en oeuvre la comparaison du signal d'évolution temporelle AT avec un seuil et

dcu representing the average brightness of the rank block u. Advantageously, the method implements the application of a one-dimensional temporal transform to Azo detection signals, ... AzM-1, calculated for current images taken at times t0, ... tN'-1, to obtain an array of transformed coefficients Do, Da ... DN'-i, and a calculation of an Ar signal representative of a temporal evolution of the image, with:

V'-! Ar = ED u = l

iv'-i Do is proportional to-; L Alli 'N

The method can be characterized in that it implements the comparison of the time evolution signal AT with a threshold and

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déclenchement d'une action de détection d'intrusion si le signal AT franchit ledit seuil.

triggering an intrusion detection action if the signal AT crosses said threshold.

The method can implement several detection signals Az corresponding to several distinct characteristic areas of the image. At least one detection signal Az can be generated from at least one characteristic area of the image.

In its first aspect, the subject of the invention is thus a method for processing a current video image in which a first signal associated with all or part of the image (called the characteristic area of the image) and representative of fixedness and variation characters of said zones at a given moment with respect to an instantaneous reference image. For at least one zone, it is then possible to produce at least one second signal representative of the evolution of the first signal in time.

Several successive sub-signals can be used to elaborate the temporal variability signal of the zone of the image from which the decision will be made. Thus the invention proposes to process the block image with which a first instantaneous signal is associated: the spatial activity As representative of the instantaneous situation of a block of the resulting image, and then an image processing by zones to which is associated a second instantaneous signal: the detection signal Az representative of the variation or the instantaneous fixity of an area of the image relative to a reference image. The invention then makes it possible to treat the zones in time by elaborating the final signal of temporal variability of the zones of the image.

In its second aspect, the invention relates to a method for detecting intrusions in a remote monitoring system, is particularly suitable for detecting an intrusion manifesting itself in the form of a misalignment and / or an obstruction of a camera.

CCTV can not be effective if the field of view of the camera, or any other sensor used, is changed by an action on the sensor so as to make invisible the events occurring in the scene. In order to detect this type of event, the method according to the second aspect of the invention is based on the modifications induced on the content of the images. The process works

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ainsi sur une scène quelconque, et ne nécessite pas la présence d'un objet ou d'un marqueur spécifique dans la scène.

so on any stage, and does not require the presence of a specific object or marker in the scene.

 For this purpose, the process preferably operates in two steps.

extraire de la scène des éléments caractéristiques, de manière à obtenir une connaissance pertinente, représentative d'une situation normale. Dans une seconde phase dite opérationnelle, le procédé exploite sa connaissance de la scène pour comparer la scène courante aux caractéristiques d'une scène normale, et ainsi effectuer une détection. The first (optional) is a learning phase. This aims to

extract characteristic elements from the scene, so as to obtain relevant knowledge representative of a normal situation. In a so-called operational second phase, the method uses its knowledge of the scene to compare the current scene with the characteristics of a normal scene, and thus perform a detection.

 The method of the invention in its second aspect is usable whenever any scene is observed by a camera pointing permanently the same field. The process itself is independent of the coding system of the image used, but it fits very easily into a standard rate reduction coding system using the Discrete Cosine Transform TCD.

exploitée. L'invention tire profit des propriétés avantageuses de la TCD pour mettre en évidence les variations spatiales et temporelles dans l'image liées à son contenu. Cette approche originale permet ainsi de se baser exclusivement sur le traitement du signal vidéo. L'invention peut mettre en oeuvre toute autre transformée par blocs linéaire et orthogonale, par exemple, la transformée de Fourier Discrète, la transformée en ondelettes (DWT) dont les coefficients ont été réorganisés pour obtenir les blocs transformés, ou bien la transformée de Hadamard, ou de Slant, ou bien encore, la transformée en sinus discrète. In a preferred embodiment, the TCD discrete cosine transform, recommended in the JPEG and MPEG standards, is

exploited. The invention takes advantage of the advantageous properties of the TCD to highlight the spatial and temporal variations in the image related to its content. This original approach thus makes it possible to rely exclusively on the processing of the video signal. The invention can implement any other linear and orthogonal block transform, for example, the Discrete Fourier transform, the wavelet transform (DWT) whose coefficients have been reorganized to obtain the transformed blocks, or the Hadamard transform. , or Slant, or again, the discrete sinus transform.

 According to its second aspect, the invention relates to a method implementing at least one video camera providing a video image, said intrusion to be detected manifesting itself in particular by a misalignment and / or an obstruction of said camera, characterized in that it sets implemented: a) a transformation, using a block transform, of the blocks of at least one set of image control blocks comprising N blocks Bn, m of dimensions M × M pixels to obtain for each block Bn, m of the transformed coefficients Fn, m (i, j), i and j varying from 0 to M-1; b) determining, for each of the N blocks Bn, m, from the transformed coefficients Fn, m (i, j), a spatial activity bsan, m,

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le calcul pour au moins une image courante 1 (t) et une image antérieure 1 (t') prises à des instants t et t'd'un facteur d'évolution de ladite activité spatiale [bsanm (t). bsan. m (t')], de chacun des N blocs Bn, m, pondéré d'un facteur représentatif de l'évolution de la luminosité d'au moins un bloc dudit ensemble de blocs, pour constituer pour chaque bloc dudit ensemble de blocs, un paramètre caractéristique d'une intrusion qui est comparé avec un seuil prédéterminé de détection d'intrusion pour détecter une intrusion.

calculating for at least one current image 1 (t) and an earlier image 1 (t ') taken at times t and t of an evolution factor of said spatial activity [bsanm (t). NWSO. m (t ')], of each of the N blocks Bn, m, weighted by a factor representative of the evolution of the brightness of at least one block of said set of blocks, to constitute for each block of said set of blocks, a characteristic parameter of an intrusion that is compared with a predetermined threshold of intrusion detection to detect an intrusion.

An intrusion detection can be declared when a predetermined number N'of blocks of the set of N control blocks reaches or exceeds at least one said predetermined threshold.

According to a first embodiment, the method is characterized in that b implements: said determination for each of the N blocks Bn, m of the current image 1 (t) and of the previous image (t ') said spatial activity, as well as a mean spatial activity [SA (t), SA (t ')], blocks of said set of blocks; calculation for the current image 1 (t) and for the previous image 1 (t ') of a factor kn, m (t) of evolution of said spatial activity bsa (t), of each of said N blocks Bn, m, - the calculation for said successive images of a factor K (t) of evolution of the mean spatial activity {(SA (t), SA (t ')} of the current image 1 (t) relative to the prior image 1 (t ') in said set of blocks.

the determination for each block Bn, m of said set of blocks of a ration parameter, m (t) characteristic of an intrusion by weighting said evolution factor kn, m (t) of the spatial activity of the blocks by said mean evolution factor K (t) of the spatial activity, which is representative of the evolution of the luminosity.

In particular, the method is characterized in that: knm (t) = bsan, m (t) / bsan, m t ') and K (t) = SA (t) / SA (t') rationm (t) = kn. m (t) / K (t) Said comparison with a predetermined threshold may implement an evaluation of the parameter rationm (t) for the current image 1 (t) and the parameter rationm (t ') for said previous image) ( t ') and a comparison

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de l'évolution de ce paramètre entre ces deux images avec au moins un seuil donné de détection d'intrusion constituant un dit seuil prédéterminé de détection d'intrusion.

the evolution of this parameter between these two images with at least one given threshold of intrusion detection constituting a said predetermined threshold of intrusion detection.

According to a second embodiment, the method is characterized in that b implements: for each block Bn, m of said zone, the computation of a bsan spatial activity parameter. m; the calculation for each block Bn, m of said set of blocks of a weighted spatial activity parameter Cn, m, representative of the contrast of said block, and the calculation for each of said blocks Bn, m of the current image 1 (t ) and the previous image 1 (t ') of a factor k'n. m (t) of evolution of said weighted spatial activity, to obtain said spatial activity evolution factor for each of said n blocks.

The method can be characterized in that:

avec dcn m (t) = Fn m (0, 0) coefficient transformé de rang (0, 0) dudit bloc Bn, m de l'image I (t), représentant la luminosité moyenne du bloc Bn, m, et p étant une constante (par exemple p entier > 2).

et en ce que : k'n, m (t) = Cn, m (t)/Cn, m (t')

Le procédé peut mettre en oeuvre une évaluation du paramètre k'n. m (t) pour l'image courante 1 (t) et du paramètre k'nm (t') pour l'image antérieure) (t') et une comparaison de l'évolution de ce paramètre entre ces deux images [I (t), ) (t')] avec au moins un deuxième seuil donné qui constitue un dit seuil prédéterminé de détection d'intrusion.

with dcn m (t) = Fn m (0, 0) transformed coefficient of rank (0, 0) of said block Bn, m of the image I (t), representing the average luminosity of the block Bn, m, and p being a constant (for example p integer> 2).

and in that: k'n, m (t) = Cn, m (t) / Cn, m (t ')

The method can implement an evaluation of the parameter k'n. m (t) for the current image 1 (t) and the parameter k'nm (t ') for the previous image) (t') and a comparison of the evolution of this parameter between these two images [I ( t),) (t ')] with at least a second given threshold which constitutes a said predetermined threshold of intrusion detection.

/\ bsan. m= EE (/)-F./)- (0)./ (0 =07=0 j The method can be characterized in that:

/ \ bsan. m = EE (/)-F./)- (0) ./ (0 = 07 = 0 d

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With a (i, j) weighting constants and p being a constant (for example p integer> 2)
According to a preferred embodiment of the intrusion detection phase, the prior image 1 (t ') is the image l (t-1) immediately preceding the current image l (t), this previous image being replaced by said current image 1 (t) when a new current image l (t + 1) appears and a said intrusion characteristic parameter has not reached or passed a predetermined threshold of intrusion detection.

 This allows refresh of the previous image that serves as a basis for comparison, as long as the situation remains normal. On the other hand, as soon as a detection threshold is crossed, the previous image is frozen, which makes it possible to improve the diagnosis.

 According to a preferred implementation, the method also implements a learning phase for selecting blocks of the image constituting said set of control blocks.

 The method can then be characterized in that the learning phase implements the selection from the set of blocks Bn, m of the image of a said set or dictionary of control blocks, for which the value of the factor d the evolution of the spatial activity remains substantially constant in a given time interval during which at least one event, chosen from among a sudden change in brightness, the extinction or the lighting of at least one lighting, and / or the moving a person in the field of the camera, as long as the camera is not subject to obstruction and / or misalignment.

 The learning phase can implement a selection of the blocks Bn, m of the image having elements oriented substantially horizontally and / or vertically.

 The method can be characterized in that it implements an obstruction detection when a decrease in the spatial activity of the image SA (t) relative to the mean spatial activity averaged over a sequence of previous images, exceeds a given threshold of activity drop and when the average spatial activity SA (t) of the blocks Bn, m of the set of control blocks of the current image has a value below a threshold of given activity.

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 Other features and advantages of the invention will become apparent on reading the following description, given by way of non-limiting example, in connection with the drawings in which: FIGS. 1 to 3 illustrate the invention under its first aspect, Figures 1 and 2 showing the calculation modules of the activity parameters Az and AT defined below, and Figure 3 giving an example of architecture of the set; FIGS. 4 to 15 illustrate the invention in its second aspect, namely: FIGS. 4 and 5 are algorithms; FIGS. 6 to 8 illustrate the influence of the criteria SA, DC and CG defined hereinafter; FIG. 9 is an example of a learning algorithm; FIG. 10 is an algorithm that details the selection of oriented blocks; FIG. 11 illustrates, in the form of an algorithm, the alarm triggering logic; FIG. 12 is an exemplary architecture, and FIGS. 13 to 15 respectively illustrate an example of an evolution of the number of blocks of the dictionary during a learning process, a detection of misalignment of the camera, and a detection. obstruction of the camera.

procédé sous son premier aspect, à partir de la représentation finale. FIGS. 1 and 2 respectively detail for the invention according to its first aspect the first development of a signal Az representative of the variation or the instantaneous fixity of an area 6 of the image, then the elaboration of the final signal AT by MOD 1 modules for the signal Az and MOD2 for the signal AT Figure 3 illustrates the decision-making according to the

process under its first aspect, from the final representation.

FIG. 1 shows a current video image 1 (t) provided by a video-surveillance camera C and a reference image Iref. These images are derived from the digital coding techniques previously mentioned. The current video image 1 (t) stored in a register 1 and the reference picture tref stored in a register 2 are of identical size, for example 256 × 256 pixels (or pixels). In the preferred example, the current video images 1 (t) and Iref are divided into 1024 blocks of M × M = 8 × 8 points.

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 Each point corresponds to a digital datum which corresponds for example to a gray level coded on an integer, for example on 8 bits. The reference image Iref is subtracted point by point from the current video image 1 (t) in order to obtain a difference image or resulting image ihres which is stored in a register 3.

 The resultant image Ires which therefore represents the difference between the snapshot and the reference image; then undergoes a block-by-block two-dimensional transform to obtain a transformed IT image representative of the differences between the current video image 1 (t) and the tief reference image. The transformed image IT is represented by a matrix MAT of coefficients Cij which is stored in a register 4.

 The transformed IT image is produced block by block for two reasons, which are on the one hand the imitation of the computing power and on the other hand with a view to block processing of the image which will be discussed later.

 However, it is possible to make a global transformation of the entire image which will be assimilated to a single block.

 The transform made in the preferred example is a discrete cosine transform (hereinafter TCD). Other linear and orthogonal transforms are usable, among others the discrete spatial Fourier transform, the Hadamard transform, the wavelet transform, etc. However, the TCD has the advantage of obtaining a number of coefficients identical to the number of points and allows the direct extraction of the average brightness of the treated block.

 The same transform can be reused according to the invention on a time stack of EN values input.

Each block of the resulting image is represented by a matrix of coefficients Cij obtained by applying the following known formula (discrete cosine transform):

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avec f (x) = Jz si x = 0 et f (x) = 1 si x * 0 V2

- dans laquelle i et j sont les indices des coefficients du bloc et sont compris entre zéro et M-1 ; et - x (n1, n2) correspond à la valeur numérisée du point de coordonnée n1, n2 dans le bloc avant transformation. where M is the maximum number of rows and columns of points in a block;

with f (x) = Jz if x = 0 and f (x) = 1 if x * 0 V2

in which i and j are the indexes of the coefficients of the block and are between zero and M-1; and - x (n1, n2) corresponds to the digitized value of the coordinate point n1, n2 in the block before transformation.

 For each block, a signal As representative of the variation or fixity corresponding to a spatial activity As of said block is extracted. This signal is stored in a register 5.

 The spatial activity As is a combination of at least some coefficients Cij of the matrix MAT constituting the transformed image IT and it can be for example defined as the sum of the squares of all the coefficients Cij of the matrix MAT resulting from the transform , in particular the TCD, except for the coefficient Coo which is representative of the mean luminosity of the block of the corresponding resulting image. This makes it possible to overcome to a large extent variations in the average brightness of the image.

 Thus, for the preferred example, we obtain a 32 x 32-pixel image representing the Asu spatial activity of all the blocks of the image.

Since according to this example, we carry out a sum of squares, we will be careful to code As on a sufficient number of bits (12 bits for the proposed example).

par la valeur Az obtenue par la formule suivante :

1 N I !''' Az=-yA.,, N,-0

Puisque l'activité spatiale As est codée sur 12 bits, il convient de coder l'activité spatiale moyenne d'une zone sur 16 bits ou plus. On obtient For each of the N blocks of the zone 7, which is a characteristic zone or a set of blocks of the image in which the intrusion detection is carried out, the average of the spatial activity of all the blocks of this zone is calculated. 7. Each zone is represented for the current image

by the value Az obtained by the following formula:

1 NI! '''Az = -yA. ,, N, -0

Since the spatial activity As is coded on 12 bits, it is appropriate to encode the average spatial activity of a zone of 16 bits or more. We obtain

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alors un signal représentatif de la variation ou de la fixité de la zone pour l'image courante obtenue par différence entre une zone de l'image courante et la même zone de l'image de référence.

then a signal representative of the variation or the fixity of the area for the current image obtained by difference between an area of the current image and the same area of the reference image.

 If this signal is null, it is because all the N blocks of the considered zone of the current image are identical to those, for the same zone, of the reference image. If this signal is weak, it is because the differences for said zone between the reference image and the current image are minimal.

 Generally, this is the translation into the transformed signal of the noise of the image or slight changes in brightness. On the other hand, when the signal is important, it is because the differences between the reference image and the current image for said zone are themselves important, and are likely to correspond to the detection of an intrusion. To obtain an improved intrusion detection, the last N '(for example N' = 8) values of the average spatial activity of said zone 9, obtained for the last N images captured, are stacked for each zone. These values are stored in a register 9.

formule suivante :

From these values, a transform is applied, in particular one-dimensional TCD. Each intrusion zone is then represented by a matrix 10 of coefficients D, obtained by applying the

following formula:

1. 0. 0 avec c ( =-=r si/= 0 et c =/si f 0 V2

- dans laquelle N'correspond au nombre maximum de valeurs empilées ; - dans laquelle u est l'indice des valeurs empilées (compris entre Cet N'-1) ; - dans laquelle f (t) est égal à la valeur de l'activité spatiale moyenne Azide ja zone choisie de l'image de rang t.

1. 0. 0 with c (= - = r if / = 0 and c = / if f 0 V2

in which N'corresponds to the maximum number of stacked values; in which u is the index of the stacked values (between this N'-1); in which f (t) is equal to the value of the average spatial activity Azide ja zone selected from the image of rank t.

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AT = 2 D ~ AT = Y, Dl 1 =i =

On obtient donc pour chaque zone un signal AT qui est stockée dans un registre 11 et qui représente les variations dans le temps de l'activité spatiale moyenne des blocs de la zone. Ce signal Ar est représentatif des différences entre les blocs des huit dernières images courantes et ceux de l'image de référence pour ladite zone. An Ar signal representing the level of variation of said zone in time, which corresponds to a temporal activity Ar defined as a combination, for example the sum of squares, of all the coefficients Du, is then extracted from the matrix stored in a register 10. of the matrix 10 resulting from the one-dimensional TCD transform, except for the coefficient C, which is representative of the average value of the N '= 8 last average spatial activities. For example, we have:

AT = 2 D ~ AT = Y, Dl 1 = i =

Thus, for each zone, an AT signal is obtained which is stored in a register 11 and which represents the variations in time of the average spatial activity of the blocks of the zone. This signal Ar is representative of the differences between the blocks of the last eight current images and those of the reference image for said zone.

 S! this signal is weak because most of the blocks in the zone are only slightly different from those of the reference image and these differences remain constant over time.

la zone sont passés d'un contenu proche de celui de l'image de référence à un contenu qui diffère beaucoup par rapport à celui de l'image de référence. La valeur numérique de ce signal sera d'autant plus important. Ce signal permet donc de quantifier les mouvements ayant lieu dans la zone dans le temps. If this AT signal is important, then one or more blocks of

the zone have changed from a content close to that of the reference image to a content that differs a great deal from that of the reference image. The numerical value of this signal will be all the more important. This signal makes it possible to quantify the movements taking place in the zone in time.

 FIG. 3 illustrates the implementation of intrusion detection. The signal AT is generated by the modules MOD1 and MOD2 from the video images provided by the camera C.

important pour être signalé par le biais d'une alarme AL. This signal AT is thus compared by a comparator COMP 13 to a threshold ATs whose value is stored in a register 12 so as to decide in a decision circuit DET whether it is a sufficiently

important to be reported through an AL alarm.

 The invention according to its first aspect thus makes it possible to signal any abnormal movement taking place in a predefined zone. By judiciously positioning one or more zones in the image, the invention makes it possible to monitor only the zones of passage required and thus minimizes the number of

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calcul à effectuer Elle permet également d'écarter du champ de la détection des parties de l'image comportant un éclairage (lampe ou autre) dont l'extinction, ou l'allumage, peut être dû à d'autres causes qu'une intrusion.

calculation to be carried out It also makes it possible to deviate from the field of detection of the parts of the image having a lighting (lamp or other) whose extinction, or ignition, may be due to other causes than an intrusion .

partie ou totalement afin de maximiser la détection. Note that it is also possible to overlay several zones

partly or totally to maximize detection.

The invention also makes it possible to signal an alarm when the camera is moved (the person skilled in the art speaks of misalignment of the camera) or when the camera lens is obstructed or moved (the person skilled in the art speaks of obstruction of the camera). camera field). In such a case, the content of the image varies a lot from one image to another, but this variation does not extend over time.

 The system can be entirely realized in software or entirely realized by means of dedicated electronic components.

 The system can be implanted on a hardware device. It uses several signal processors (called DSPs of the English Digital Signal Processor) to perform image subtraction functions, TCD transformations by blocks and to calculate the spatial and temporal activities.

 The comparators, averaging and decision making are performed by a module on the DSP processor. It is important to point out that the system must process the information in real time or, failing that, at the minimum rate of images acquired and processed per second and per camera. Indeed, since one seeks to detect a movement, it is necessary that the time difference between two successive images processed is not too important to retain this movement in the image at the time of acquisition. A frame rate of one frame per second and per camera is sufficient in this respect.

 The invention presents a method and a system that performs automatic presence detection using, in particular, coding methods and modules used for low bit rate digital transmission, as is found in the field of remote monitoring.

 According to its second aspect, the invention relates to a method which is more particularly adaptable to the detection of misalignment and / or obstruction of a surveillance camera.

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 The misalignment of a camera can be defined as an action on the camera having the effect of modifying the area of the monitored scene (rotation of the camera on its support, displacement).

 The field obstruction of a camera can be defined as an action to occupy all or part of the image by an obstacle or object placed in front of a camera lens. We speak respectively of total or partial obstruction.

 The input data of the detection method according to the second aspect of the invention is a sequence of images.

 FIG. 4 gives the block diagram of the detector of misalignment and / or obstruction of camera. A misalignment alarm, and / or partial obstruction alarm or a total obstruction alarm can be triggered when the method estimates that the detection conditions are met.

en blocs de MxM pixels, dont certains sont des blocs témoins. Les blocs témoins sont définis d'une part par un contenu remarquable, incluant certains éléments caractéristiques, et d'autre part par l'invariance de ce contenu. The process is based on the principle of subdivision of the image

in blocks of MxM pixels, some of which are control blocks. The control blocks are defined on the one hand by a remarkable content, including certain characteristic elements, and on the other hand by the invariance of this content.

 The control blocks are advantageously selected during a learning phase, which, when it is implemented, takes place under normal conditions, that is to say in the absence of an alarm situation. The position of the control blocks is stored, and this set is called a dictionary.

 When the learning phase is over, the operation phase starts. When a misalignment or obstruction, a number of the control blocks change content. An alarm is triggered based on the number of witness blocks that have changed content.

 The algorithm relies on the principle of invariance of the control blocks in the image in the absence of an abnormal situation. When a misalignment or obstruction occurs, a number of these feature blocks lose their stability, and a decision criterion detects a change in the content of a block.

 The algorithm is therefore organized in two distinct phases (Figure 5): learning phase and exploitation phase.

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The following paragraphs detail respectively the criteria of detection of a change of contents of a block of the image, the phase of learning, then the phase of exploitation.

The criterion of change of content in the image is based on a set of parameters characterizing the content thereof. The tool used to calculate these parameters is a linear and orthogonal transform such as the Discrete Cosine Transform (TCD).

In the bidirectional case and for an image subdivided in blocks of M x M pixels, the TCD makes correspond to a block M x M pixels a block of M x M coefficients. These express the frequent representation of the amplitude of the luminance of the pixels. The TCD transform is expressed by the following mathematical formulation:

/-l /-l F (O, 0) =--/ (-) (2) M"\=o\=0

c (u) =l/V2 siu=0 c (u) = stuO Pour ii, X. \'. v=0,... M-L, par exemple M =8. et (u, v) index de la fréquence spatiale horizontale et verticale, (x, y) = index de position horizontale et verticale bloc de pixel f (x, y) luminance du pixel au point (x, y) dans l'image.

/ -l / -l F (O, 0) = - / (-) (2) M "\ = o \ = 0

c (u) = l / V2 siu = 0 c (u) = stuO For ii, X. \ '. v = 0, ... ML, for example M = 8. and (u, v) index of the horizontal and vertical spatial frequency, (x, y) = horizontal and vertical position index pixel block f (x, y) luminance of the pixel at the point (x, y) in the image .

F (u, v) coefficient transformed at the point (u, v).

The characterization parameters of the spatio-temporal content use the TCD coefficients.

Spatial information is provided from the TCD transformed domain by computing the spatial activity of the image. This parameter precisely informs about the details in the indexed picture (fit) at time t. The image (batch) contains (HxW) blocks of pixels. The size of the block fn, m (x, y) is M2 pixels.

= (.m (x. y) / x. y = 0. M - \ / n = 0 .. H-Im = O .. W-lf (3)

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Dans un premier temps la transformée TCD est appliquée à l'image entière. Le premier facteur représentatif du contenu de l'image, l'activités spatiale, est obtenu à partir de l'image transformée DCT (If).

At first, the TCD transform is applied to the entire image. The first factor representative of the content of the image, the spatial activities, is obtained from the transformed image DCT (If).

A very active image block spatially has a consequent representation in the transformed domain. Its coefficients have a large amplitude and spectral distribution.

Où (H x W) est le nombre de blocs de l'image. 1 D (il) = t) m (u, v) = DCT (fn, m (x, y)) / LI, v, x, y = 0 .. lvl-1 / n = O, B. -1 / m = O, B .. W-1}

Where (H x W) is the number of blocks in the image.

Two parameters are introduced, (bsa) and (SA (/ f)). They respectively represent intra-block and intra-image spatial activity. The analyzed content is that of each pixel block fn, m (x, y) positioned at n, m in the image (ist).

/\ = (/- (. T')) = E i : ( ('))'- (F (0, 0))' (5) n'in = bsu (f,,,,, (X, Y) E 1 (Fn&commat;m '=00"

SA (lot) également noté SA (t) est l'expression du contenu global de l'image (t) à l'instant t. Il est calculé par une moyenne des (bs,,,) concernés.

The indicator (bsanm) takes into account the alternative components of the transformed coefficient block Fn. m (us v)

/ \ = (/ - (. T ')) = E i: ((')) '- (F (0, 0))' (5) n'in = bsu (f ,,,,, (X, Y) E 1 (Fn &commat; m '= 00 "

SA (batch) also denoted SA (t) is the expression of the overall content of the image (t) at time t. It is calculated by an average of (bs ,,,) concerned.

Où (N x M) est le nombre des pixels de l'image (en considérant qu'au début de l'apprentissage, tous les blocs sont des blocs témoins). (7,) = b. s ,,, (7,) = --- 'b. , (6) A xMu ,,, = o N x Mn = () ppl = o

Where (N x M) is the number of pixels in the image (considering that at the beginning of the learning, all the blocks are control blocks).

The SA (ist) indicator qualifies the richness of the image.

This parameter is affected by rate reduction coding and transmission errors.

By way of illustration, we take the capture of images taken with artificial lighting (a), just after extinction of the light (transitional phase of rehabilitation of the camera (b), a little later compared to the extinction of

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l'éclairage (lorsque la caméra s'est réadaptée) (c), puis après allumage (d) et après réadaptation (e) suite à l'allumage. Si on calcule l'activité spatiale bsan, m des blocs de ces images, on constate que l'activité spatiale varie en fonction de la luminosité.

the lighting (when the camera has readjusted) (c), then after lighting (d) and after re-adaptation (e) following ignition. If one calculates the spatial activity bsan, m blocks of these images, one notes that the spatial activity varies according to the luminosity.

 FIG. 6 shows the temporal evolution of the average block spatial activity (SA) on a sequence of 290 images, affected by the events a to e above, then by a depointing / obstructlon event from the image 260. The extinction (b) and ignition (d) phases of artificial lighting, and of rehabilitation (c) and (e) are clearly identified. On the other hand, one can also note a transient fall or rise in SA just after the sudden change in luminosity. This corresponds to the re-adjustment time of camera C under the new conditions.

 The spatial activity parameters are therefore sensitive to the content of the image, but also to rapid and / or significant variations in brightness.

son premier aspect.

The definition of the intra-block spatial activity bsa chosen in the preceding paragraph can be generalized to any formula using the coefficients of the TCD. This consideration is also valid for the spatial activity As or Asu defined above in the context of the invention under

its first aspect.

Où aU,}) est un coefficient constant de pondération de la composante utilisée, et p une constante (par exemple p entier 2)
Plus largement, une activité spatiale peut être calculée à partir des résultats de toute transformation d'image linéaire et orthogonale appliquée bloc par bloc d'image (transformée en ondelettes, de Haar, de Hadamard,...). 7 7 .. = ZE (./)-J/))'- (a (0. 0). ,,, (0, 0)) (7) - "/ ="

Where aU,}) is a constant coefficient of weighting of the component used, and p a constant (for example p integer 2)
More broadly, a spatial activity can be calculated from the results of any linear and orthogonal image transformation applied block by image block (transformed into wavelets, Haar, Hadamard, ...).

 Another use of the transformed coefficients, makes it possible to define a type of parameter called contrast local block cnm, sensitive to the contents of the image but little to the variations of luminosity.

 The coefficient (0,0) of the transform, for example TCD, for example of each block, is used for this purpose:

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=M (8)

dcn m représente la luminosité moyenne du bloc à la position (n, m).

= M (8)

dcn m represents the average brightness of the block at position (n, m).

DC (/ f) is the expression of the overall content of the image. It is calculated by an average of the activities (bsan, m) concerned.

La figure 7 illustre les variation du paramètre DC pour la même séquence que la figure 6. L'allure de la courbe est sensiblement la même que dans le cas de la figure 6, c'est-à-dire qu'on distingue la trace des différents évènements intervenus au cours de la séquence. 1 It 1 If 1 "" nn M = or) ((11) = dC 1/1 // (11) = LE (C tr m (9)

FIG. 7 illustrates the variation of the parameter DC for the same sequence as in FIG. 6. The shape of the curve is substantially the same as in the case of FIG. 6, that is to say that the trace is distinguished different events that occurred during the sequence.

From an activity parameter such as bsan, we can define a local block contrast Cn, m

p étant une constante (par exemple p entier 2.

p being a constant (for example p integer 2.

This results in a medium block contrast CBM.

CM (/,) =c,,,, (/,) =--- 1 (1.) (11) NxM x n=O 111=0

Un contraste global de l'image CG (ID peut être défini sur le même principe, mais à partir des paramètres globaux DC et SA (IJ.

For the image It, this contrast CBM (ist) is defined as:

CM (/,) = c ,,,, (/,) = --- 1 (1.) (11) NxM xn = O 111 = 0

An overall contrast of the image CG (ID can be defined on the same principle, but from the global parameters DC and SA (IJ.

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The evolution of the parameter CG for the same sequence as that used previously for FIGS. 6 and 7 is represented in FIG. 8. It can be seen that only the misalignment intervened in FIG. 260 causes a significant change in the value of CG.
The contrast parameters are therefore sensitive to the content of the image and only to it, regardless of brightness variations.

To define a criterion for detecting a change in content, the principle is to verify that either the spatial activity or the contrast vary little from one image to another, by avoiding luminosity variations.

In either case, this amounts to a weighting by a factor representative of the brightness.

 In the first case, the criterion for detecting a content change based on the spatial activity block bsa (n m) corresponding to the block (n, m) must be as free as possible from the variations in brightness, since the value spatial activity depends on the brightness.

préférence l'image immédiatement précédente (t-1). Le rapport k n, m (t) des deux valeurs est utilisé :

zu = /,/ (13)

Pour définir une signature caractéristique des éléments contenus dans l'image, et en principe indépendante de la luminosité, il faut s'efforcer de compenser les effets des variations de luminosité sur l'activité spatiale bloc. To detect a change of content, we will examine the evolution of the bsan spatial activity, m (t) characterizing a block of a current image, at the date t, with respect to a previous image 1 (t '), who is from

preferably the immediately preceding image (t-1). The ratio kn, m (t) of the two values is used:

zu = /, / (13)

To define a characteristic signature of the elements contained in the image, and in principle independent of the luminosity, one must endeavor to compensate the effects of the variations of luminosity on the block spatial activity.

 The spatial activity decreases with the illumination, despite the device for automatic adaptation of the camera, a kn factor, specific to each block. However, a brightness variation is global, that is to say that each block of the image is normally affected in the same way.

We can then estimate that, in the case of a change in illumination, the spatial activity change factor knm of a block is the same as the variation of

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facteur K (t) de changement d'activité spatiale moyenne SA entre l'image 1 (t) et l'image I (t') à l'instant t avec : K (t) = SA (t)/SA .

K (t) factor of mean spatial activity change SA between image 1 (t) and image I (t ') at time t with: K (t) = SA (t) / SA.

The mean spatial activity SA is defined above.

défini de la manière suivante : On a :

ratzan (/) = kn, /K (t) = [/ )]/ [SA (t)/SA (t')] (14)

Le critère caract, est défini comme la valeur maximale

caract (t),,,,, = max [r/o, (tJ/r/o, (t')]-1 (15)

Il s'agit de la valeur maximale du rapport entre ration, m (t) et ration. m (t') à laquelle on retranche le chiffre 1, c'est-à-dire : si ration, m (t) > ration. m (t'), alors caractn,m(t) =(ratio(t) / ratio(t'))-1 si ration m (t) < ration (t'0, alors caractn,m(t) =(ratio(t') / ratio(t))-1 si ration, m (t) = ration. (f), alors caractn,m (t) = 0
De la sorte, cette valeur est toujours supérieure ou égale à 0.
Le critère caractnm décide qu'un bloc de l'image change de contenu entre deux images de type / dont l'une est une image courante 1 (t)

et l'autre une image antérieure 1 (t') lorsque la variation d'activité bloc est très différente de la variation d'activité image (définie comme l'activité spatiale moyenne des de l'image). La valeur de caractn. est toujours positive ou nulle, que la luminosité augmente ou diminue. Cette propriété permet de n'utiliser qu'un seuil de détection au lieu de deux On considère qu'un bloc a changé de contenu lorsque cette caractéristique a atteint une certaine valeur seuilVariationContenu : For blocks n, m whose kn factor, m is similar to K (t), we can say that their content has changed little from one image to another, despite taking into account the effects of variation in brightness. Conversely, for blocks whose content has changed, the values k (nm (t) and K (t) will be very different.) The criterion character m, which is used preferentially to characterize the change of content independently of the brightness is

defined as follows:

ratzan (/) = kn, / K (t) = [/)] / [SA (t) / SA (t ')] (14)

The criterion character, is defined as the maximum value

character (t) ,,,,, = max [r / o, (tJ / r / o, (t ')] - 1 (15)

This is the maximum value of the ratio between ration, m (t) and ration. m (t ') to which we subtract the number 1, that is to say: if ration, m (t)> ration. m (t '), then character, m (t) = (ratio (t) / ratio (t')) - 1 if ration m (t) <ration (t'0, then character, m (t) = ( ratio (t ') / ratio (t) - 1 if ration, m (t) = ration. (f), then character, m (t) = 0
In this way, this value is always greater than or equal to 0.
The criterion character decides that a block of the image changes content between two images of type / one of which is a current image 1 (t)

and the other anterior image 1 (t ') when the variation of block activity is very different from the variation of image activity (defined as the average spatial activity of the image). The character value. is always positive or zero, whether the brightness increases or decreases. This property makes it possible to use only one detection threshold instead of two It is considered that a block has changed content when this characteristic has reached a certain threshold valueVariationContent:

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c//c > seuilVariatlon Contenu
On peut utiliser bien entendu un autre critère tenant compte de ratio (t) et de ratio (t'). On peut par exemple utiliser ratio (t) /ratio (t') et mettre en ooeuvre deux seuils, qui constituent deux seuils d'intrusion prédéterminés.

c // c> thresholdVariatlon Content
One can of course use another criterion taking into account ratio (t) and ratio (t '). One can for example use ratio (t) / ratio (t ') and implement two thresholds, which constitute two predetermined thresholds of intrusion.

An illustration of the effectiveness of this criterion is given below.

If one calculates the parameter character, m of the comparison of the block spatial activity with compensation of the effects of variation of brightness, between two images, one taken in artificial lighting and the other after extinction of this lighting and rehabilitation, it can be seen that the use of the compensation of the effects of variation of brightness makes it possible to limit the differences not related to a change of content. Some blocks, however, are not affected by the variation in brightness, especially those around the computer monitors turned on.

simple. En effet, le contraste est indépendant de la luminosité, donc la comparaison peut être faite directement :

, ) =c)/c,, ) (17)

On utilise préférentiellement un critère défini comme :

c/-, = max [. (1), 1/') -1 (18)

Il s'agit de la valeur maximale du rapport entre les deux valeur kn, m (t () et le kn, m (t') à laquelle on retranche 1, de sorte que ce rapport est toujours supérieur ou égal à 0. In the second case, the use of a contrast-based parameter to define a content variation detection criterion is more

simple. Indeed, the contrast is independent of the brightness, so the comparison can be done directly:

)) = c) / c ,,) (17)

A criterion defined as:

c / -, = max [. (1), 1 / ') -1 (18)

This is the maximum value of the ratio between the two values kn, m (t () and the kn, m (t ') to which we subtract 1, so that this ratio is always greater than or equal to 0.

c//Y/c/' (,) > seuit Variation Contenu (18') The criterion character detects a variation of content of a block Sol pa following condition is realized:

c // Y / c / '(,)> threshold Variation Content (18')

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En général, pour calculer le paramètre caraco m ou caract'n. m, on choisit l'image courante (t) à l'instant t et l'image précédente l (t-1) à l'instant t'= t-1. On peut également choisir une autre image antérieure 1. Lorsque le seuil défini en (18') est dépassé, on cesse de mettre à jour l'image

précédente qui sert de base à la comparaison L'organigramme de la phase d'apprentissage est représenté sur la figure 9. Il est appliqué pour chaque nouvelle image présentée à l'entrée. Elle met en oeuvre trois étapes : une présélection des blocs de l'image, l'élimination des blocs instables, et enfin la mise à jour du dictionnaire D par l'élimination des blocs instables repérés.

In general, to compute the parameter caraco m or character'n. m, the current image (t) is selected at time t and the preceding image l (t-1) at time t '= t-1. One can also choose another previous image 1. When the threshold defined in (18 ') is exceeded, one stops updating the image

previous which serves as a basis for comparison The flowchart of the learning phase is shown in Figure 9. It is applied for each new image presented at the entrance. It implements three steps: a pre-selection of the blocks of the image, the elimination of unstable blocks, and finally the updating of the dictionary D by the elimination of the unstable blocks identified.

 After the processing of each image, a test determines whether the learning is complete, and whether the resulting dictionary D can be used by the operation phase.

 During the learning phase, the control blocks are selected and their location in the image is stored. Control blocks are those that contain characteristic elements and whose contents do not change in the sense of one or the other characteristic criterion. m or character m defined above.

 The set of control blocks forms a reference dictionary D.

 For this, the changes occurring between two images separated by a duration defined according to the needs (for example 1 second) are observed block by block. Any block detected once by the criterion character. m or by the criterion character m is eliminated from the dictionary D of the control blocks.

murs, embrasure de porte, etc Toutefois, suivant la scène observée, d'autres structures caractéristiques peuvent être choisies. The control blocks can be selected as having remarkable content. This characteristic is highlighted during the step of preselecting the control blocks. This takes place once, at the beginning of the learning phase. This step consists in selecting the blocks of structured content, in particular containing horizontal or vertical elements net. These are indeed common in the images (furniture,

walls, doorway, etc. However, depending on the scene observed, other characteristic structures can be chosen.

For vertical and horizontal elements, the proposed solution is the following (see the flowchart in Figure 10).

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The mask filters Sobel [1-1] (horizontal and [1-1] '(vertical)) are separately applied to the pixel image f, and the activity of the resulting pixel image whose contour has been thus isolated by the Sobel filters is calculated for each block Bn, m according to formula (5) or (7). We thus obtain two images of the oriented block spatial activity, respectively horizontal as (boHm) and vertical asbo, and of average activity oriented ASoH and ASov calculated according to (6).

 To select the blocks of sufficient directed activity, the largest oriented activity (among the horizontal and vertical oriented activity) is selected, and the latter is required to be greater than one (FS) representing a ratio between the oriented activity of the considered block and the average oriented spatial activity of the image.

1 S (who H (n. m) > who/n, m)) et si . s'. > S'H. alors on sélectionne le bloc simon si . s'//./M,) > F,. alors sélectionner le bloc (19)
Les blocs sélectionnés à l'issue de ce traitement forment un premier dictionnaire qui va être affiné sur une longue période et de nombreuses images par les étapes suivantes. FSH is the threshold for the vertically oriented activity, and FSv for the horizontally oriented activity:

1 S (who H (n. M)> who / n, m)) and if. s. >S'H. then we select the simon block si. s' //./ M,)> F ,. then select the block (19)
The blocks selected at the end of this treatment form a first dictionary which will be refined over a long period and many images by the following steps.

 The control blocks are also characterized by an invariant content between two images. A criterion for detecting a content change of a block, based on the spatial activity of the block, has been defined previously. This criterion is applied here: any location of the image where a change of content is detected in a block is eliminated from all the control blocks.

 This step is iterated over many images and for a sufficient time to place the system in a range of wide operating conditions, representative of the conditions that will normally be encountered during an operating phase. For example, the learning phase may include brightness variations (such as those of the day / night cycle), or other disturbances such as moving objects in

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une partie du champ de la caméra, ou une intrusion dans une zone de passage, l'allumage ou l'extinction d'une lumière ou d'un écran d'ordinateur etc...

a part of the camera's field, or an intrusion in a passage zone, the lighting or extinction of a light or a computer screen etc ...

 Another important task is carried out at this stage: it is also a question of updating the image which will be compared with the next image that will be presented For that, one simply memorizes the current image, because no alarm is supposed occur during the learning phase.

 The learning phase ends when the expected duration has expired The duration of the learning phase must cover a period long enough to include the maximum of disturbing phenomena.

However, in order to shorten this delay, it is also possible to stop the learning phase if the dictionary obtained has not changed for a long time (for example several hours).

Taille (Dictionntire) > proportionBlocsDansDico x (H x W), (21) où (H x W) est le nombre de blocs de l'image. The condition stopping learning is therefore:
Duration learning durationMaxiPhaseLearning or DurationDicoInvariant # durationMaxiNo Change (20)
Finally, it remains to verify that the learning phase has succeeded, that is to say that the dictionary is valid. For that, it suffices that it reaches a sufficient size, or in other words that there is in the image a significant number of control blocks. This condition makes it possible in particular to avoid random triggering of alarms during the operating phase: a typical example is that of a scene in which the passage zone of the objects concerns almost the entire image. This situation results in a very small number of stable blocks. During the exploitation or detection phase, the change of content of some blocks will be enough to trigger a false alarm
The condition validating the obtained dictionary is:

Size (Dictionnary)> proportionBlocksInDico x (H x W), (21) where (H x W) is the number of blocks in the image.

 A module can detect the particular situations of total obstruction. It operates completely independently of

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détecteur de dépointage/obstruction partielle. Afin de différencier le cas d'une obstruction totale de celui d'une scène entièrement noire ou uniforme, on considère que : - une obstruction totale du champ d'une caméra est un phénomène quasi instantané (l'image passe d'une image ayant un contenu à une tmage noire de façon Instantanée) ; - une image noire provenant d'une scène non éclairée est obtenue progressivement.

detachment detector / partial obstruction. In order to differentiate the case of a total obstruction from that of an entirely black or uniform scene, it is considered that: - a total obstruction of the field of a camera is an almost instantaneous phenomenon (the image passes from an image having content at a black temage Instantly); - a black image from an unlit scene is obtained gradually.

It is on this postulate that the detection of a total obstruction is based. If we abruptly pass from an image containing information to a black image, we consider that there is total obstruction. On the other hand, if the change is progressive, we do not consider the event as a total obstruction.

First, we summarize the past evolutions by calculating the average S / oy of 'space activity image SA (t), on the MEM ~ IMG ~ AS images previously arrived at the current image, and on which no total obstruction n has been detected.

Une baisse de luminosité de l'image est interprétée comme une obstruction totale si on a à la fois une forte baisse de l'activité spatiale Saul) par rapport à l'activité spatiale moyenne SAmoy. une valeur faible de l'activité spatiale SA (t), et une valeur significative de l'activité spatiale moyenne passée sAmoy . avec par exemple : :

. S/) (/) x2 < , et S'/f (t) < seuilActiBlocMinimale (23) et < " M () . SeMllActcBlocMlc , IE ; IG 4iS - S ... (1) = ------ I; SA (tr) (22) A / C. t / At / Gf;

A decrease in brightness of the image is interpreted as a total obstruction if there is both a sharp drop in Saul spatial activity) compared to the average SAmoy spatial activity. a low value of the spatial activity SA (t), and a significant value of the mean spatial activity passed sAmoy. with for example:

. S /) (/) x2 <, and S '/ f (t) <thresholdActiBlocMinimal (23) and <"M (). SeMllActcBlocMlc

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acceptable, par exemple à la tombée de la nuit, le détecteur suspend toute surveillance de la variation du contenu des blocs témoin, et se met en veille. Lorsque les conditions sont redevenues favorables (éclairage artificiel ou lever du jour), le détecteur se réactive automatiquement et compare le contenu de la nouvelle image à celui de la dernière image exploitable avant la mise en veille. Ainsi, tout dépointage survenu durant la période d'obscurité sera détecté.

An operation of the misalignment / obstruction detector can be obtained under boundary conditions, as follows:
A module aims to eliminate situations in which the analyzed image is too rich, and in particular too dark, to make any reliable decision triggering alarm. For this, we use a condition of image brightness, which results in sufficient spatial activity of the image. When the brightness drops below the threshold

acceptable, for example at dusk, the detector suspends any monitoring of the variation of the contents of the control blocks, and goes to sleep. When the conditions are favorable again (artificial lighting or daybreak), the detector reactivates automatically and compares the contents of the new image to that of the last exploitable image before the standby. Thus any mishandling that occurred during the dark period will be detected.

SA (t)> seu! ! Act! BlockMlnlmale (24)
When the condition (24) is not satisfied, the misalignment / obstruction alarm is eventually reduced to FALSE, and an operation flag impossible set to TRUE. Typically, this case occurs when the observed scene is not lit at night. The detector waits for the next image to eventually reactivate the detector.

 The exploitation phase consists of monitoring the control blocks designated by the reference dictionary resulting from the learning. For that, one applies the same criterion of detection of change of contents between two successive images 1 that during the learning.

 The method counts the number of witness blocks of the dictionary, having changed content. If this number is significant, it detects a misalignment / obstruction of the camera. However, instability of reference dictionary blocks may occur when a sudden change in brightness has not allowed time for the camera to restore a nominal image. Therefore, in order to limit false alarms, a misalignment / obstruction event will only actually be reported if it is detected on at least two consecutive (or more) occasions.

 The flowchart of the operating phase is shown in Figure 11. As for the learning phase, it is applied for each image presented at the input.

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 There are also the identical modules of activation or deactivation of the detector, and detection of the total obstruction.

 The other two stages have many points in common with the learning phase.

 The first of these two steps is the identification of the blocks of the dictionary D resulting from the learning that are unstable in the observed scene. The process of triggering the misalignment / obstruction alarm is the second.

 The detection of the variable content blocks is performed by calculating the characteristic parameter of the content change character, m or character The process is identical to that of the learning phase.

 During the learning phase, the algorithm focused on keeping only the blocks Bn, m that did not change content from one image to another, according to the criterion character or character, m . These control blocks are characteristic of the scene vis-à-vis attempts of misalignment or obstruction.

on détecte un dépointage/obstruction de la caméra : nbreBlocsDicoDetectes > proportionBlocsAlarm x Taille (Dictionnaire), Si cette condition est réalisée, l'alarme est déclenchée. During the exploitation phase, the algorithm counts the number of control blocks having changed content between two images which are either the current image and a previous image, notably the immediately preceding image, namely the current image and an image. reference that is optionally refreshed periodically. If this number is important,

detecting a misalignment / obstruction of the camera: #BlocksDicoDetects> proportionBlocsAlarm x Size (Dictionary), If this condition is realized, the alarm is triggered.

prochaine image courante qui sera captée
Un exemple de mise en oeuvre du procédé est donné en figure 12. Le Processeur de calcul du Critère de détection de Changement de Contenu (PCCC) fournit le paramètre caractn, m (t) ou caract'n, m (t). Ce paramètre est calculé comme décrit ci-dessus, en se basant exclusivement sur l'activité spatiale bsan, m (t) et sur des blocs de taille égale à la taille couramment utilisée pour le codage des images, c'est-à-dire M = 8. This module also updates the image that will be compared to the next image that will appear. For this purpose and according to a preferred mode, the current image 1 (t) is simply memorized, but only on the condition that a misalignment / obstruction event has not been detected, and this current image will serve as a basis of comparison. to the

next current image that will be captured
An example of implementation of the method is given in FIG. 12. The calculation processor of the Content Change Detection Criterion (PCCC) provides the parameter character, m (t) or character n, m (t). This parameter is calculated as described above, based exclusively on the spatial activity bsan, m (t) and on blocks of size equal to the size commonly used for coding the images, i.e. M = 8.

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During the learning phase, the Partial Docking / Obstruction Detection Processor (PDDO) gradually builds the dictionary D by eliminating the blocks having changed content and therefore can not be part of the control blocks. During the operation phase the PDDO counts the blocks of this dictionary having changed content, possibly to trigger an alarm.

 The PDIU and PDOT processors operate in parallel with the PCCC and PDDO processors. The PDIU processor indicates to the processor PDDO so the image is unusable, and in this case, it disables the indicator of misalignment and / or partial obstruction.

Example
For an indoor scene, which can be artificially lit, and in which people can enter the field of the camera, 256 x 256 video frames were processed in blocks of 8 x 8 sizes, making a total of 1024 blocks.

 After a phase of detection of oriented blocks, the dictionary has 613 blocks (see Figure 13).

 During the learning phase during which the non-stable blocks are eliminated, the dictionary D stabilizes around 400 blocks (exactly 411 blocks).

 The vast majority of blocks in a transit zone have been eliminated.

 Outside the passage zone, other blocks have been eliminated: they are the ones that became unstable during a change of lighting during the learning process.

 The selected blocks, kept in the dictionary D, lose their stability in case of misalignment and / or obstruction, which allows a specific detection.

 Figure 14 illustrates the detector behavior.

When no event disturbs the image, the number of blocks for which a change of activity is noted is low or zero (between 0 and 2).

 In the naked image 14, a first peak (27 blocks) in 1 corresponding to the passage of a person is detected. This detection is not taken into account to trigger the alarm.

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In image na 30, an increase 11, then a stabilization 111 of the number of detected active blocks appears. It is due to a start of misalignment of the camera.

In the naked image 38, a large peak IV of the number of active blocks (100 blocks) corresponds to a frank misalignment and the triggering of the misalignment alarm.

It is therefore expected that, during a misalignment, the number of active blocks (for which the activity is detected by the evolution of the criterion character or character) becomes very important, which allows a specific detection of a misalignment, whatever the lighting variations.

moyenne AS de l'image dès que l'objectif est obstrué (image nu 2). Figure 15 illustrates the case of a total obstruction of the camera. The image changes from an image with a content to an entirely black image (image # 23). We detect a sharp drop V of the activity

AS average of the image as soon as the lens is obstructed (naked image 2).

In case of progressive decrease of the luminosity of a scene filmed (by progressive passage of the light of day to the night), the fall of the average activity of the image is not brutal, but on the contrary very progressive. In such a case, there is no triggering of an alarm.

Claims (6)

1 Intrusion detection method in a remote monitoring system using at least one video camera providing

 a video image, said intrusion to be detected manifesting itself in particular by a misalignment and / or obstruction of said camera, characterized in that it implements. a) transforming, by means of a block transform, the blocks of a set of image control blocks comprising N blocks Bnm of dimensions M × M pixels for obtaining for each block Bn, m transformed coefficients Fn, m (i, i and j varying from 0 to M-1, b) determining, for each of the N blocks Bn, m, from the transformed coefficients Fn (i, j), of a bsan spatial activity, m, - the computation for a current image 1 (t) and a previous image 1 (t ') which serves as a basis of comparison, of an evolution factor of

 said spatial activity [bsan, m (t), bsan, m (t ')], of each of the N blocks Bnm, weighted by a factor representative of the evolution of the brightness of at least one block of said set of blocks , for constituting for each block of said set of image blocks, a parameter characteristic of an intrusion that is compared with at least one predetermined threshold of intrusion detection to detect an intrusion.  2. Method according to claim 1, characterized in that it implements a detection of an intrusion when the characteristic parameter of a predetermined number N'of blocks of the set of N control blocks reaches or exceeds at least one said predetermined threshold.  3. Method according to one of claims 1 or 2, characterized

 in that b implements: - said determination for each of the N blocks Bn, m of said spatial activity bsa [bsan, m (t), bsan, m (t ')], as well as of a mean spatial activity [ SA (t), SA (t ')] blocks Bn, m of the set of blocks; calculation for the current image (ist) and for the previous image (I (t ') of a factor kn, m (t) of evolution of said spatial activity bsan m (t), of each of said N blocks Bn, m of said set of blocks, n, m; - the calculation for said successive images of a factor K (t) of said evolution of the mean spatial activity {(SA (t), SA (t ')} of the current image (I (t)) with respect to the previous image (I (t ')) in said set of blocks, <Desc / Clms Page number 33>

 your determination for each block Bn. m of said set of blocks of a ration parameter, m (t) characteristic of an intrusion by weighting said evolution factor kn, m (t) of the spatial activity of the blocks by said average evolution factor K (t ) of the spatial activity, which is representative of the evolution of the luminosity. 4. Process according to claim 3, characterized in that: knm (t) = bsan m (t) / bsan m (t '); K (t) = SA (t) / SA (t ') and ration m (t) = kn m (t) / K (t) 5. Process according to claim 4, characterized in that said comparison with a predetermined threshold implements an evaluation of the ration parameter. m (t) for the current image 1 (t) and the ration parameter, m (t ') for said previous image 1 (t') and a comparison of the evolution of this parameter between these two images with at least one given intrusion detection threshold which constitutes a predetermined threshold of intrusion detection 6. Method according to one of claims 1 or 2, characterized in that b implements:

 for each block Bn, m of said area, calculating a spatial activity parameter bsan, m; the calculation for each block Bn, m of said set of blocks of a weighted spatial activity parameter cn. m, representative of the contrast of said block, and the calculation for each of said N blocks, and for the current picture 1 (t) and the previous picture 1 (t '), of a factor k'nm (t) of evolving said weighted spatial activity to obtain said spatial activity evolution factor for each of said N blocks of said set of blocks.  7. Method according to claim 6, characterized in that:

 with dCnm = Fnm (O.O) transformed coefficient of rank (0,0) of said block Bn, m of the image at time t, representing the mean brightness of the block n. m, P constant (for example p integer # 2) and k'n, m (t) = Cn m (t) / Cn m (t ') 8. Method according to claim 7, characterized in that it implements an evaluation of the parameter k'n. m (t) for the current image 1 (t) and the parameter k'nm (t ') for the previous image) (t') and a comparison of <Desc / Clms Page number 34>

 the evolution of this parameter between these two images) (t), 1 (t ') with a given second threshold of intrusion detection which constitutes a said predetermined threshold of intrusion detection. Method according to one of the preceding claims, characterized in that:

 ..... = f Z E ((.. (') "- ((O-O),. (0. 0)" "

 p being a constant. 10. Method according to one of the preceding claims, characterized in that the prior image 1 (t ') is the image l (t-1) immediately preceding the current image 1 (t), this image being replaced by the current image 1 (t) when a new current image l (t + 1) appears and a said characteristic parameter of an intrusion has not reached or exceeded at least one said predetermined threshold of intrusion detection.  11. Method according to one of the preceding claims, characterized in that it implements a learning phase for selecting blocks of the image constituting said set of control blocks.  12. Method according to claim 11, characterized in that the learning phase implements the selection from the set of Bnm blocks of the image of a said set or dictionary (D) of control blocks, for which the value of the evolution factor of the spatial activity remains substantially constant in a given period of time during which at least one event, chosen from among a sudden variation in brightness, the extinction or the lighting of at least one lighting, and / or the movement of a person in the field of the camera, as long as the camera is not subject to obstruction and / or misalignment.  13 Method according to one of claims 11 or 12, characterized in that it implements a selection of blocks Bn, m of the image having elements oriented substantially horizontally and / or vertically.  14. Method according to one of the preceding claims, characterized in that it implements an obstruction detection when a decrease in the spatial activity of the image SA (t) relative to the mean spatial activity averaged over a sequence of previous images exceeds a given threshold of drop in activity, and when the spatial activity SA (t) of the blocks <Desc / Clms Page number 35>  Bnm of the set of indicator blocks in the image has a value below a given activity threshold.