WO2020063189A1 - Procédé et dispositif d'extraction de trajectoire de cible vidéo - Google Patents

Procédé et dispositif d'extraction de trajectoire de cible vidéo Download PDF

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WO2020063189A1
WO2020063189A1 PCT/CN2019/101273 CN2019101273W WO2020063189A1 WO 2020063189 A1 WO2020063189 A1 WO 2020063189A1 CN 2019101273 W CN2019101273 W CN 2019101273W WO 2020063189 A1 WO2020063189 A1 WO 2020063189A1
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image
pixel
transparency
foreground
pixels
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PCT/CN2019/101273
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English (en)
Chinese (zh)
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蔡昭权
蔡映雪
陈伽
胡松
黄思博
李慧
胡辉
陈明阳
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惠州学院
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20036—Morphological image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30232—Surveillance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory

Definitions

  • the present disclosure belongs to the field of image processing, and particularly relates to a method and a device for extracting a video target track.
  • the present disclosure provides a video target trajectory extraction method, including the following steps:
  • I k is the RGB color value of the unknown pixel Z k
  • the foreground pixel F i is the m foreground pixels closest to the unknown pixel Z k
  • the background pixel B j is also the m closest to the unknown pixel Z k Background pixels, the foreground and background pixel pairs (F i , B j ) totaling m 2 groups;
  • ⁇ takes a value of 0.1, and the set of foreground and background pixel pairs corresponding to MAX (n ij ) with the highest reliability is selected as (F iMAX , B jMAX );
  • S600 Superimpose grayscale information on the first image to generate a second image, and divide the second image into all its foreground pixel sets, all background pixel sets, and all unknown pixel sets;
  • steps S200 to S500 For the second image, perform steps S200 to S500 to determine a first transparency mask of the second image, and use the first transparency mask of the second image as a second transparency mask of the first image. ;
  • step S900 Extract the foreground objects in the first image of the video according to the first transparency mask of the first image modified in step S800, and retrieve the specified objects from all the foreground objects, and then follow the chronological order of all frames. , Generating a trajectory of the specified target based on all images including the specified target.
  • a video target track extraction device including:
  • a first dividing module configured to divide, for a first image in a video, all foreground pixel sets F, all background pixel sets B, and all unknown pixel sets Z in the image; wherein the first image is from the A frame of image extracted from the video;
  • a first metric module configured to: given certain foreground and background pixel pairs (F i , B j ), measure the transparency of each unknown pixel Z k according to the following formula
  • I k is the RGB color value of the unknown pixel Z k
  • the foreground pixel F i is the m foreground pixels closest to the unknown pixel Z k
  • the background pixel B j is also the m closest to the unknown pixel Z k Background pixels, the foreground and background pixel pairs (F i , B j ) totaling m 2 groups;
  • a second metric module configured to: for each of the m 2 groups of foreground and background pixel pairs (F i , B j ) and their corresponding The confidence level n ij of the foreground and background pixel pair (F i , B j ) is measured according to the following formula:
  • ⁇ takes a value of 0.1, and the set of foreground and background pixel pairs corresponding to MAX (n ij ) with the highest reliability is selected as (F iMAX , B jMAX );
  • a calculation module for calculating an estimated transparency value of each unknown pixel Z k according to the following formula
  • a determining module configured to: according to the transparency estimation value of each unknown pixel Z k Determine a first transparency mask of the first image initially;
  • a second dividing module configured to: superimpose grayscale information on the first image to generate a second image; and divide the second image into all its foreground pixel sets, all background pixel sets, and all unknown pixel sets;
  • Recalling a module for: for the second image, calling the first measurement module, the second measurement module, the calculation module, and the determination module again to determine the first transparency mask of the second image, and The first transparency mask of the second image is used as the second transparency mask of the first image;
  • a correction module configured to: use the second transparency mask of the first image to modify the first transparency mask of the first image
  • An extraction module configured to extract the foreground target in the first image of the video according to the first transparency mask of the first image obtained by the correction module, retrieve the specified target among all foreground targets, and then follow all frames In chronological order, the trajectory of the specified target is generated based on all images including the specified target.
  • the present disclosure can comprehensively utilize the credibility and gray level information of the foreground and background pixel pairs to provide a new video target trajectory extraction scheme.
  • FIG. 1 is a schematic diagram of a method according to an embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of a device according to another embodiment of the present disclosure.
  • an embodiment herein means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present disclosure.
  • the appearances of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they independent or alternative embodiments that are mutually exclusive with other embodiments. Those skilled in the art can understand that the embodiments described herein may be combined with other embodiments.
  • FIG. 1 is a schematic flowchart of a video target trajectory extraction method according to an embodiment of the present disclosure. As shown, the method includes the following steps:
  • the first image when the video foreground object is extracted, the first image may be: when the video is played, in response to a user operation, pausing the current video playback, and immediately intercepting the current frame of the paused picture so that Obtain a first image; the first image may also be: when a video is not played, in response to a user operation, randomly select a certain frame or frames in the video, and use a certain frame image as the first image.
  • this method can be used for foreground target extraction of each frame of image in the video.
  • the first image is a first frame image in a video.
  • I k is the RGB color value of the unknown pixel Z k
  • the foreground pixel F i is the m foreground pixels closest to the unknown pixel Z k
  • the background pixel B j is also the m closest to the unknown pixel Z k Background pixels, the foreground and background pixel pairs (F i , B j ) totaling m 2 groups;
  • the selection of m can make the corresponding foreground and background pixel pairs be partial samples or exhaust the entire image; as for step S200, it is intended to pass the color of the unknown pixels and the foreground background
  • the color relationship of pixel pairs is used to estimate the transparency of unknown pixels.
  • the selection of m can further combine the characteristics of neighbor pixels and unknown pixels in terms of color, texture, grayscale, brightness, and spatial distance;
  • ⁇ takes a value of 0.1, and the set of foreground and background pixel pairs corresponding to MAX (n ij ) with the highest reliability is selected as (F iMAX , B jMAX );
  • Step S300 uses the credibility to further filter the foreground and background pixel pairs, and is used in the subsequent steps to estimate the unknown pixel transparency by further filtering the foreground and background pixel pairs;
  • this embodiment naturally determines the first transparency mask of the first image naturally; the reason why it is natural is that the transparency mask can be viewed On the grounds Those corresponding pixels selected according to a certain value (or value range);
  • S600 Superimpose grayscale information on the first image to generate a second image, and divide the second image into all its foreground pixel sets, all background pixel sets, and all unknown pixel sets;
  • this embodiment considers that, in addition to the role of RGB color, each pixel should consider the effect of gray information on the pixel; therefore, after superimposing the gray information, use the following steps to modify the transparency mask.
  • steps S200 to S500 For the second image, perform steps S200 to S500 to determine a first transparency mask of the second image, and use the first transparency mask of the second image as a second transparency mask of the first image. ;
  • step S900 Extract the foreground objects in the first image of the video according to the first transparency mask of the first image modified in step S800, and retrieve the specified objects from all the foreground objects, and then follow the chronological order of all frames. , Generating a trajectory of the specified target based on all images including the specified target.
  • the present disclosure provides a new video target trajectory extraction scheme by comprehensively using the reliability and gray level information of the foreground and background pixel pairs. It can be understood that in this solution, the extraction of video foreground objects is an infinite approximation process. Due to the transition of color and gray in the video image frame, it is difficult to say that the transparency mask obtained by some method is the only one. correct. Theoretically, the above-mentioned embodiment integrates more information and considers more factors, which is helpful for a more comprehensive examination of the images in the video, thereby extracting a relatively satisfactory video foreground target.
  • the specified target is derived from a photo, or from an image database.
  • the designated target is a criminal suspect
  • the photo is a recent photo of the criminal suspect
  • the image database is a database of wanted person images.
  • step S900 the method further includes the following steps:
  • S11001 Binarize the first transparency mask of the first image after the correction of the previous frame, and take a threshold value of 0.5 to obtain a first binary image of the foreground target;
  • the true corresponding pixels in the second binary image are used as all foreground pixel sets F c
  • the false corresponding pixels in the third binary image are used as all background pixel sets B C
  • the remaining pixels are used as all unknown pixel sets Z. C.
  • the first transparency mask of the modified first image divides all foreground pixel sets F c , all background pixel sets B C and all unknown pixel sets Z C in the first image corresponding to the current frame, so that it can be used in image processing Strike a balance between accuracy and efficiency; that is, this embodiment has the inherited characteristics: it inherits the transparency mask of the previous frame, and uses the transparency mask to divide the foreground pixel set and background of the next frame The pixel set and the unknown pixel set. In view of the continuity and similarity in the picture content, this division not only relies on the transparency mask of the previous frame but also uses the means of morphological erosion and morphological expansion, which belong to the disclosure An innovation point.
  • step S600 the grayscale information is superimposed on the first image to generate a second image in the following manner:
  • the first image and the third image generate a second image by using the following formula:
  • IM 2 represents the gray value of the k-th pixel on the second image after superimposition
  • x r represents a neighborhood pixel of the k-th pixel x k on the first image
  • N k represents the neighborhood of the neighborhood centered on x k Number of pixels
  • ⁇ is taken as 0.5.
  • step S800 further includes:
  • step S802 further includes:
  • pixels Z dp with different positions including two cases: A pixel Z dp2 at the edge of the second transparency mask and a pixel Z dp1 at the edge of the first transparency mask;
  • this embodiment additionally pays attention to pixels with different positions in the edges determined by the two transparency masks, and finds these pixels at different positions from each other;
  • the edge corresponding to each mask can be regarded as a connected or closed curve to a certain extent, no matter how the closed curves corresponding to the two masks overlap or not overlap: For those pixels on the edges corresponding to the two masks that do not correspond (that is, the positions are different, or the positions are not coincident), the closed area enclosed by the edges and edges of the two masks is jointly determined, and Positions of all closed pixels of the closed area;
  • step S8024 a first transparency mask for correcting the first image is used. That is, this embodiment is similar to the modified idea of the previous embodiment, except that this embodiment solves the area enclosed by the edges corresponding to the two masks. Taking the pixel Z dp1 as an example, it belongs to a pixel of a first transparency mask of a first image, and there is an estimated transparency value in the first transparency mask of the first image.
  • the pixel The pixel in the second image corresponding to the position of Z dp1 has the transparency value in the second image.
  • the transparency estimation value and the average value of the transparency value are used as the corrected transparency estimation value of the corresponding pixel Z dp1 .
  • the pixels Z dp1 are similar.
  • the present disclosure also discloses a video target track extraction device in another embodiment, including:
  • a first dividing module configured to divide, for a first image in a video, all foreground pixel sets F, all background pixel sets B, and all unknown pixel sets Z in the image; wherein the first image is from the A frame of image extracted from the video;
  • a first metric module configured to: given certain foreground and background pixel pairs (F i , B j ), measure the transparency of each unknown pixel Z k according to the following formula
  • I k is the RGB color value of the unknown pixel Z k
  • the foreground pixel F i is the m foreground pixels closest to the unknown pixel Z k
  • the background pixel B j is also the m closest to the unknown pixel Z k Background pixels, the foreground and background pixel pairs (F i , B j ) totaling m 2 groups;
  • a second metric module configured to: for each of the m 2 groups of foreground and background pixel pairs (F i , B j ) and their corresponding The confidence level n ij of the foreground and background pixel pair (F i , B j ) is measured according to the following formula:
  • ⁇ takes a value of 0.1, and the set of foreground and background pixel pairs corresponding to MAX (n ij ) with the highest reliability is selected as (F iMAX , B jMAX );
  • a calculation module for calculating an estimated transparency value of each unknown pixel Z k according to the following formula
  • a determining module configured to: according to the transparency estimation value of each unknown pixel Z k Determine a first transparency mask of the first image initially;
  • a second division module configured to superpose the grayscale information on the first image to generate a second image, and divide the second image into all its foreground pixel sets, all background pixel sets, and all unknown pixel sets;
  • Recalling a module for: for the second image, calling the first measurement module, the second measurement module, the calculation module, and the determination module again to determine the first transparency mask of the second image, and The first transparency mask of the second image is used as the second transparency mask of the first image;
  • a correction module configured to: use the second transparency mask of the first image to modify the first transparency mask of the first image
  • An extraction module configured to extract the foreground target in the first image of the video according to the first transparency mask of the first image obtained by the correction module, retrieve the specified target among all foreground targets, and then follow all frames In chronological order, the trajectory of the specified target is generated based on all images including the specified target.
  • each module may be combined with a processor and a memory to form a system for implementation; however, FIG. 2 is not a hindrance: each module may also have a processing unit itself to implement data processing capabilities.
  • the apparatus further includes the following modules:
  • a module is sequentially called for: extracting each remaining frame image from the video and using it as the first image, respectively, and sequentially calling: the first division module, the first measurement module, and the second measurement A module, a calculation module, a determination module, a second division module, a re-call module, a correction module, and an extraction module to extract all foreground targets of the video; or include:
  • the inheritance calling module is configured to extract each remaining frame image from the video, use it as the first image, and input it to a third partitioning module, where the third partitioning module is configured according to the above
  • the first transparency mask of the corrected first image of one frame divides all foreground pixel sets F c , all background pixel sets B C and all unknown pixel sets Z C in the first image corresponding to the current frame; then,
  • the inheritance calling module sequentially calls the first measurement module, the second measurement module, the calculation module, the determination module, the second division module, the recall module, the correction module, and the extraction module in order to extract all foreground targets of the video, where
  • the third division module includes:
  • a first binary image processing unit configured to binarize the first transparency mask of the first image corrected in the previous frame, and take a threshold of 0.5 to obtain a first binary image of the foreground target;
  • a second binary image initial unit configured to: use the first binary image as an initial value of the second binary image
  • a second binary image processing unit configured to: perform a morphological erosion operation on the second binary image by using a circular structural element with a size of 3x3, and update the second binary image with the obtained result;
  • the first repeated calling unit is used to repeatedly call the second binary processing unit five times;
  • a third binary image initial unit configured to: use the first binary image as an initial value of the third binary image
  • the third binary image processing unit is configured to perform a morphological expansion operation on the third binary image using a circular structure element of size 3x3, and update the third binary image with the obtained result:
  • a true / false division unit configured to: use the corresponding corresponding pixels in the second binary image last updated by the second binary image processing unit as true to set all foreground pixel sets F c ; Corresponding false pixels in the three binary image are all background pixel sets B C and the remaining pixels are all unknown pixel sets Z C.
  • the second dividing module further includes:
  • An average filtering unit configured to: perform average filtering on the first image to obtain a third image
  • a second image generating unit is configured to generate the second image by using the following formula:
  • IM 2 represents the gray value of the k-th pixel on the second image after superimposition
  • x r represents a neighborhood pixel of the k-th pixel x k on the first image
  • N k represents the neighborhood of the neighborhood centered on x k Number of pixels
  • ⁇ is taken as 0.5.
  • the correction module further includes:
  • Finding an edge unit configured to find the edge of the second transparency mask and the edge of the first transparency mask respectively according to the second transparency mask of the first image and the first transparency mask of the first image;
  • a position determining unit configured to obtain the positions of all pixels at the edges of the second transparency mask, and the positions of all pixels at the edges of the first transparency mask, and determine the positions of all pixels at the edges of the second transparency mask and An area where the positions of all pixels of the edges of the first transparency mask are coincident, and then pixels Z sp having the same position are determined;
  • the first correction unit is configured to find the transparency estimate value of the first transparency mask corresponding to the first image of the pixel Z sp and the transparency estimate value of the second transparency mask corresponding to the first image, respectively.
  • the average value of the pixel is used as the estimated transparency value of the pixel Z sp ;
  • the second correction unit is configured to correct the first transparency mask of the first image by using the pixel Z sp corrected transparency estimation value.
  • the device can implement the method described in the first embodiment.
  • the location determining unit further includes:
  • Different position subunits are used to further determine pixels with different positions Z dp according to the positions where all the pixels of the edges of the second transparency mask and the positions of all the pixels of the edges of the first transparency mask overlap, including: : A pixel Z dp2 located at the edge of the second transparency mask and a pixel Z dp1 located at the edge of the first transparency mask;
  • a closed subunit configured to use the pixels Z dp at different positions and the pixels Z sp at the same position to obtain the edge determined by the second transparency mask and the edge of the first transparency mask: A closed closed area, and the positions of all closed pixels of the closed area;
  • the complex correction subunit is configured to: modify the first transparency mask of the first image in combination with the pixel Z dp1 modified transparency estimation value and the pixel Z dp2 modified transparency estimation value.
  • each functional unit may be integrated into one processing unit, or each unit may exist alone, or two or more units may be integrated into one unit.
  • the above integrated unit may be implemented in the form of hardware or in the form of software functional unit. When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.
  • the technical solution of the present disclosure essentially or part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium Including instructions for causing a computer device (which may be a smart phone, a personal digital assistant, a wearable device, a notebook computer, or a tablet computer) to perform all or part of the steps of the method described in various embodiments of the present disclosure.
  • the aforementioned storage media include: U disks, Read-Only Memory (ROM), Random Access Memory (RAM), mobile hard disks, magnetic disks, or optical disks, and other media that can store program codes .

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Abstract

La présente invention concerne un procédé et un dispositif d'extraction de trajectoire de cible vidéo. Tout d'abord, une valeur de transparence estimée est recalculée par mesure de la crédibilité de paires de pixels de premier plan et d'arrière-plan pour obtenir un premier masque de transparence d'une première image ; deuxièmement, une nouvelle image est générée en superposant des informations de gris pour obtenir un second masque de transparence de la première image et en modifiant en outre le premier masque de transparence de la première image ; enfin, le premier masque de transparence modifié est utilisé pour extraire des cibles de premier plan d'une certaine trame d'image dans une vidéo, une cible spécifiée est extraite à partir de toutes les cibles de premier plan, et la trajectoire de la cible spécifiée est générée sur la base de toutes les images comprenant la cible spécifiée conformément à la séquence temporelle de toutes les trames. L'invention peut utiliser de manière complète les informations de crédibilité et de gris des paires de pixels de premier plan et d'arrière-plan dans une certaine trame d'image dans la vidéo, de façon à fournir un nouveau schéma d'extraction de trajectoire de cible vidéo.
PCT/CN2019/101273 2018-09-26 2019-08-19 Procédé et dispositif d'extraction de trajectoire de cible vidéo WO2020063189A1 (fr)

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PCT/CN2019/088278 WO2020062898A1 (fr) 2018-09-26 2019-05-24 Procédé et appareil d'extraction de cible d'avant-plan d'une vidéo
PCT/CN2019/101273 WO2020063189A1 (fr) 2018-09-26 2019-08-19 Procédé et dispositif d'extraction de trajectoire de cible vidéo
PCT/CN2019/105028 WO2020063321A1 (fr) 2018-09-26 2019-09-10 Procédé de traitement vidéo fondé sur l'analyse sémantique et dispositif associé
PCT/CN2019/106616 WO2020063436A1 (fr) 2018-09-26 2019-09-19 Procédé et appareil d'analyse d'un comportement d'apprentissage de salle de classe basé sur un apprentissage profond (dnn)

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PCT/CN2019/088278 WO2020062898A1 (fr) 2018-09-26 2019-05-24 Procédé et appareil d'extraction de cible d'avant-plan d'une vidéo

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