WO2014009291A1 - Système et procédé de détection d'occupation basés sur la vision - Google Patents

Système et procédé de détection d'occupation basés sur la vision Download PDF

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Publication number
WO2014009291A1
WO2014009291A1 PCT/EP2013/064312 EP2013064312W WO2014009291A1 WO 2014009291 A1 WO2014009291 A1 WO 2014009291A1 EP 2013064312 W EP2013064312 W EP 2013064312W WO 2014009291 A1 WO2014009291 A1 WO 2014009291A1
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Prior art keywords
gradient
occupancy detection
detection
image
circles
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PCT/EP2013/064312
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English (en)
Inventor
Harshit Tiwari
Cristian Andreola
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Osram Gmbh
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Publication of WO2014009291A1 publication Critical patent/WO2014009291A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects

Definitions

  • the present invention relates to a system and method of vi ⁇ sion based occupancy detection using head-top circles.
  • the inventive method has an occupancy detection algorithm for in ⁇ door lighting control that detects humans in videos captured through a ceiling mounted camera utilizing a head-top circle detection-based approach.
  • the method is scale and illumina ⁇ tion invariant and works well even for top-view videos cap ⁇ tured through a fish-eye lens-camera system.
  • the human head is visible consistently from a ceiling mounted camera, and due to near spherical shape of human head, the projection of head in 2-D plain (e.g. the camera plane) is always near cir ⁇ cular (disc) in shape.
  • the present method utilizes this char ⁇ acteristic of the human head top to accomplish occupancy de ⁇ tection in a circle detection-based framework.
  • PIR motion sensors have been used to detect different human motion events. Heat signatures are captured for each of these events.
  • a PIR motion sensor with ultra low power consumption has been known in the art.
  • a motion sensing device has been designed to switch off lights when there is no motion.
  • the hardware design has been opti- ⁇
  • a fusion of audiovisual features has been proposed to improve the accuracy of acoustic event detection system.
  • a human identification technique using acoustic micro-doppler signatures and band-pass sampling technique and gaussian mix ⁇ ture model-based human identification technique was also pre ⁇ sented by Z. Zhang and A. G. Andreou ("Human Identification Experiments Using Acoustic Micro-Doppler Signatures", The Ar ⁇ gentine Conference on Micro-Nanoelectronics , Technology and Applications, wholesome Aires, Argentina, Sept, 2008.).
  • the ac ⁇ curacy of acoustics based human detection systems was not satisfactory enough for commercial deployment. It is also very difficult to analyze the pose/fall of a person and their unusual activities with acoustic system.
  • a method to detect human head-tops in videos is known using Kalman filter and mean-shift tracking.
  • the technique employs the image color intensity information and the Local Binary Pattern (LBP) to construct a fourdimensional histogram representative of the color intensity values and the texture of the target under study.
  • LBP Local Binary Pattern
  • the new object location is then determined by Mean Shift iteration after the predict location is confirmed by Kalman filter. Color, texture, and motion features are integrated to track objects.
  • the 2D occupancy system for determining a position of a user includes a host device and a plurality of mo ⁇ tion detection devices wherein the host device and the plu ⁇ rality of motion detection devices are connected through a network.
  • Each motion detection device has a viewing angle and the viewing angle of any motion detection device overlaps with the viewing angle of at least one other motion detection device .
  • occupancy detection and measurement, and obstacle detection using imaging tech ⁇ nology is disclosed. Embodiments include determining occu ⁇ pancy, or the presence of an object or person in a scene or space. If there is occupancy, the amount of occupancy is measured .
  • Ceiling mounted cameras offer a cheap alternative in terms of cost as well as computation complexity as it can be readily integrated to existing ceiling fixtures (e.g., luminaires, smoke detectors, air-conditioner vents, etc) .
  • the present method is fully automatic in the sense it does not require any additional manual inputs such as trip-wires or manual triggers for detection.
  • the projection of a head on a 2-D plane is al ⁇ ways near-circular in shape.
  • This characteristic of the human head-top is used to accomplish occupancy detection in a cir ⁇ cle detectionbased framework.
  • Described herein is a system for occupancy detection techniques exploiting the centre point's distribution for head- top circles in a region.
  • the system includes atleast one ceiling mounted camera having fish-eye lens, a controller for executing an algorithm using ceiling mounted cameras that generates occupancy detection associated with the detection of human head tops predicted movement of occupants within each of the plurality of segments.
  • the algorithm is a vision- based occupancy detection algorithm using gradient back propagation approach through ceiling mounted cameras which utilizes the circularity of head shape for accurate detec ⁇ tion.
  • the system further includes an output operably con ⁇ nected to the controller to communicate the occupancy esti ⁇ mates generated by the algorithm.
  • vision- based occupancy detection algorithm using gradient back propagation approach in conjunction with Centre Points Accu- mulation technique for detection of imperfect circles with specific application to head-top detection.
  • vision-based occupancy detection algorithm using gradient back propagation approach in conjunction with homogeneity criterion for detection of imperfect homogenous circles with spe ⁇ cific application to head-top detection.
  • a method of detecting occupancy in a region exploiting the centre points distribu ⁇ tion for head-top circles in a region includes modelling using occupancy detection algorithm using ceiling mounted cameras. Due to near-spherical shape of a human head, the projection of a head on a 2-D plane (e.g. the camera plane) is always near-circular in shape. The characteristic of the human head-top is used to accomplish occupancy detec ⁇ tion in a circle detection-based framework.
  • the method fur ⁇ ther includes calculating modelbased predictions of occupancy within the region by detecting human head-tops by locating circles in grayscale images. The circles are detected by back propagating the gradients in the image and applying thresholds on accumulated points thereby detecting circle centres.
  • the points are accumulated in a 3-D accumulator array of size (pl,ql,r3), where pi & ql are the width and the height of the image respectively and r3 is the number of radius values.
  • the value of r3 depends upon the range of radius values used for circle detection.
  • a system for detecting occupancy in a region using centre points distribution for head-top circles in a region includes means for detecting each of the plurality of head top circles in a par- ticular region.
  • the system further includes means for calculating model-based detections of occupancy within the region based on state equations that model occupancy of each region, detecting human head-tops by locating circles in grayscale images, further the circles are detected by back-propagating the gradients in the image and applying thresholds on accumu ⁇ lated points thereby detecting circle centres.
  • the points of the potential centre candidates are accumulated in a 3-D ac ⁇ cumulator. After accumulation, a suitable threshold is applied to each 2-D (x,y) plane in accumulator array to detect circles of corresponding radii.
  • a computer readable storage medium encoded with a machine-readable computer pro ⁇ gram code for generating occupancy detection for a region
  • the computer readable storage medium including instructions for causing a controller to implement the said method.
  • the computer program includes instructions for calculating model- based detections of occupancy within the region based on state equations that model occupancy of the region, detecting human head-tops by locating circles in grayscale images, fur ⁇ ther the circles are detected by back-propagating the gradi ⁇ ents in the image and applying thresholds on accumulated points thereby detecting circle centres.
  • the points of the potential centre candidates are accumulated in a 3-D accumu- lator.
  • a suitable threshold is applied to each 2-D (x,y) plane in accumulator array to detect circles of corresponding radii.
  • the parametric definition of a circle is used in terms of centre coordinates and radius (x, y, r) to model the head-top circle as an im ⁇ perfect circle with x, y and r as random variables.
  • Circle centre coordinates obtained by gradient back propagation al ⁇ gorithm form points in the (x, y, r) space. The circle centre points are accumulated in 3-D space to obtain the actual head-top circles.
  • the human head-tops appear as ho ⁇ mogenous objects in top-view videos.
  • the said property is used to distinguish head-top circles from random circles formed due to clutter in the scene.
  • Fig 1 illustrates an overview of the disclosed system.
  • the Controller (1) connected to a ceiling mounted video camera (2) having fish-eye lens system.
  • the Controller also controls the switches for lighting/HVAC according to occupancy detec- tion.
  • Fig 2 illustrates gradient back propagation for circular ob- j ects
  • Fig 3 illustrates circle detection using gradient back propa ⁇ gation
  • Fig 4 illustrates distribution of centre candidates for dif ⁇ ferent values of radius
  • Fig 5 illustrates a 3-D volume for concentrating centre candidates along (x, y, r) dimensions
  • Fig 6 illustrates homogeneity criterion gradient sums using circular integrator filters
  • Human occupancy detection is an integral component for various control appli- cations e.g. automatic lighting control, HVAC control etc.
  • the method gives a detection accuracy of 90% (Lower of the two: Recall rate- 90% & Precision rate- 95%) .
  • Experiments have been carried out for top-view videos of 15 people taken at two different locations, with varying illumination, ceiling height and ambience.
  • the head-top de ⁇ tection in top-view videos is performed utilizing a novel circle detection-based approach.
  • the work specifically fo- cuses on a novel extension to the head-top detection algo ⁇ rithm which improves the accuracy of the overall system by about 7%.
  • 'region' is used throughout the description to refer to both a region as well as various sub-divisions of the re ⁇ gion.
  • the term 'region' refers to both the area in general as well as to the individual sub-regions or zones e.g. rooms. Therefore, generating occupancy detection for the region would include generating occupancy estimates for each of the individual zones.
  • an occupancy detection for a region may include data such as a number of occupants within the region, a probability associated with all possible occupancy levels associated with the region changes in occupancy, detecting something (human) ; catching sight of human, data indicative of the reliability of confidence associated with occupancy (e.g., covariance) , as well as other similarly useful data related to occupancy.
  • the application discloses a system and method for detecting occupancy based on exploiting the centre point's distribution for head-top circles.
  • occupancy is detected based solely on Gradient Back Propagation Ap ⁇ proach.
  • This application expands upon the scope of the prior art, disclosing additional embodiments and methods of imple ⁇ menting occupant detection.
  • Occupancy detection has traditionally been accomplished using either vision-based (e.g., using mast/ceiling mounted cameras or non vision-based techniques such as PIR, Ultrasonic, acoustic, etc.
  • Vision-based occupancy detectors have been known to be cost effective and power efficient for both in ⁇ door/outdoor scenarios, where a camera is mounted on a mast with a clear view of entry/exit points or locations with movement of people.
  • these sensors can be used to not only detect human occupation but they can also be useful for people counting, detection of unusual activity or possible fall of a person (e.g., in assisted living sce ⁇ narios) .
  • top-view videos there are no rich features available for human detection.
  • the conventional features like the skin color (hue) , the face of the person, the shape and the move ⁇ ment of the limbs, etc. cannot be used in this view.
  • a lot of human detection techniques rely on facial features and limb shape/movements for detecting human presence.
  • head-top is the only feature that is visible consis ⁇ tently in all poses for human detection.
  • the problem of human detection boils down to human head-top detection in top-view.
  • a human head resembles solid sphere in shape and the projection of a sphere is always circular at all angles. Head-top detection thus implies finding circles (disks) in top-view videos.
  • a circle detection algorithm for grayscale images utilizing the gradient direction alongwith the gradient magnitude is disclosed.
  • the algorithm also pre ⁇ sents a fixed computational complexity for a given image resolution .
  • the Fig. 2 illustrates the gradient back propagation for cir ⁇ cular objects.
  • the disclosed algorithmic approach detects hu ⁇ man head-tops by locating circles in grayscale images. The circles are detected by back-propagating the gradients in the image and applying thresholds on accumulated points thereby detecting circle centers. The points are accumulated in a 3-D accumulator array of size (pl,ql,r3), where pi & ql are the width and the height of the image respectively and r3 is the number of radius values. The value of r3 depends upon the range of radius values used for circle detection. After accu- mulation, a suitable threshold can be applied to each 2-D
  • the gradient back-propagation approach does not yield a unique center candidate. Instead, it returns a region around the actual circle center with all the points in the region satisfying the circle detection threshold.
  • perfect (near-perfect) circles one can get clear local maxima in the accumulator array at the centre of the circle. Hence the other centre candidates can be suppressed in the region by finding local maxima around the centre in the accu ⁇ mulator array.
  • the region around the centre in the accumulator ar ⁇ ray typically consists of a distribution of points with high accumulation values.
  • a simple approach to find local maxima may not fetch the exact centre coordinates. Similar distribution is observed for different values of radius close to the actual radius value. This distribution of accumulation values along the image axes (x,y) for different radii values for the original image in fig.l has been depicted in fig.4.
  • Fig. 4 illustrates the distribution of centre candidates for different values of radius. It is clear from fig. 5 that for imperfect circles, candidate center points are distributed in a 3-dimensional space constituting the 2 image axes (x,y) and a third radial axis. For a 2-dimensional distribution of cir ⁇ cle points, one can concentrate the centre candidates using a mexican hat-shaped 2-D filter. This essentially finds the centroid of the distribution of the center candidates in the 2-D accumulator space. A threshold can be applied to the fil- tered accumulator array to find the actual center points. We can apply the same principle in 3-D space (x,y, radius) to find centroid of the distribution of the center candidates.
  • a 3-D filter as shown in fig 5 which illus- trates a 3-D volume for concentrating centre candidates along (x,y, radius) dimensions.
  • an ellipsoid has been selected as the 3-D volume for integrating center candidates in the accumulator array, which is consistent with the choice of a circular fil ⁇ ter along the 2-D image axes (x,y) .
  • the span of the volume is longer along the radial axis as found through experiments that the distribution of center candidates is spread across a longer range on the radial axis compared to the image axes.
  • the ellipsoid shaped 3-D filter is applied to accumulate the potential center candidates in the 3-D (x,y,r) accumulator array.
  • the accumulation values at all points within the said ellipsoid in the neighborhood of a point in the accumulator array are integrated.
  • a threshold is then applied to the in ⁇ tegrated value at each pixel location to find actual circle centers. This technique yields about 7% improvement in the detection accuracy for head-top circles.
  • the direction of the gradient vector is used, along with its magnitude, in circle detection framework.
  • the gradient vector can be represented as (gr, ⁇ ) in the polar coordinate system, wherein gr is the magnitude of the gradi ⁇ ent and & is the direction.
  • gr is the magnitude of the gradi ⁇ ent
  • & is the direction.
  • the gradient vectors at the object bound ⁇ ary appear to emerge out of the object along radial lines as shown in fig . 2.
  • rl & r2 are integer values representing the minimum and maximum radius values respectively in terms of number of pixels.
  • the possible radius values for the chosen range are integers be ⁇ tween (rl, r2) .
  • r3 be the no. of radius values.
  • the accumulator array gets populated with the gradient values of the original grayscale image.
  • a suitable threshold can then be applied to each 2-D (x,y) plane in accumulator array to detect corresponding circles of radii between (rl, r2) .
  • the entire process is depicted in fig. 3.
  • the choice of threshold depends on the circularity of the object to be de- tected. For near perfect circles in the image, a high value of threshold is preferable, whereas, if the circles in the original image are imperfect or incomplete, it is better to choose a low value for this threshold.
  • the head-top circles as can be observed in the original im ⁇ age fig. 2, are homogenous in nature i.e.
  • the pixels in the head-top part of the image have similar intensity or gray ⁇ scale values.
  • the property of head-top circles is used to im ⁇ prove the detection performance of our algorithm by applying a homogeneity constraint on the detected circles. This helps in eliminating false positive detection of circles formed with boundaries of heterogeneous objects or due to excessive clutter.
  • There are a lot of different criteria to check the homogeneity of an object in an image some of the simple ones being -variance/standard deviation of pixel values within the object, number of edge points within the object in the edge- map of the image, sum of gradient values within the object etc. Any one of these methods can be used depending upon the type of homogeneity to be checked.
  • the sum of gradients as the homogeneity criteria is chosen as the gradients on the grayscale image for earlier steps has been already computed. After detecting circles in the image, gradients are inte- grated for all the points within the detected circles in the grayscale image. A suitable threshold is then applied to the gradient sums to find head- top circles among all the de ⁇ tected circles. The gradient sums can also be calculated up ⁇ front by applying circular 2-D filters on calculated gradient array for the grayscale image. This approach yields gradient sums for all the points in the image. The gradient sum values can be found at detected circle centres and apply threshold only on those selected values.
  • a novel head-top detection al ⁇ gorithm is disclosed utilizing gradient back-propagation in grayscale images for use in conjunction with ceiling mounted camera (s) [at least one camera or a plurality of camera (s) operably connected] for occupancy detection applications.
  • the algorithm performs person detection in top-view videos captured through ceiling mounted cameras having fisheye lens- camera system.
  • the Centre Points Accumulation tech- nique is used for detection of imperfect circles has been proposed with specific application to head-top detection. This has been suggested as an extension to the said head-top detection algorithm utilizing gradient back-propagation.
  • a homogeneity criterion for detection of imperfect homogenous circles is also disclosed with specific applica ⁇ tion to head-top detection. This has been suggested as an ex ⁇ tension to our head-top detection algorithm utilizing gradient back-propagation.
  • the proposed technique detects head- tops in grayscale images for use in conjunction with ceiling mounted cameras for occupancy detection applications.
  • the said algorithm and the pro ⁇ posed techniques are used in conjunction for improving the output result.
  • the algorithm performs person detection in top-view videos captured through ceiling mounted cameras hav- ing fisheye lens-camera system. This view has certain advan ⁇ tages like occlusions are minimum and the coverage is good i.e.
  • a database of top- view videos has been generated by installing a ceiling mounted camera in a discussion room and a lab inside our of ⁇ fice premises.
  • the database captures variability in poses, head-shapes, ceiling height, and illumination conditions.
  • the developed algorithm has been tested using the generated data ⁇ base and a detection accuracy of 90% has been achieved. This method outperforms the Hough circles-based approach for cir ⁇ cle detection as it utilizes more useful information about circular shapes as compared to the Hough-circles-based ap ⁇ proach .

Abstract

L'invention concerne un système et un procédé de détection d'occupation destinés au contrôle de l'éclairage/de systèmes CVC. Le système utilise l'algorithme basé sur la vision pour détecter les cercles supérieurs de têtes humaines dans des vidéos de vue en plan capturées par une caméra fixée au plafond. Les images en couleurs converties en images en niveaux de gris ainsi qu'un filtre à gradient sont appliqués pour trouver des vecteurs de gradients à chaque point de l'image. Le vecteur de gradient est ensuite propagé vers l'arrière pour trouver le centre candidat. L'accumulation des points centraux et la contrainte d'homogénéité sont utilisées en combinaison avec la rétropropagation de gradient afin de trouver le centre candidat unique pour des objets quasi circulaires tels qu'une tête humaine.
PCT/EP2013/064312 2012-07-12 2013-07-05 Système et procédé de détection d'occupation basés sur la vision WO2014009291A1 (fr)

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WO2017163883A1 (fr) * 2016-03-22 2017-09-28 日立ジョンソンコントロールズ空調株式会社 Climatiseur
CN110349199A (zh) * 2019-06-25 2019-10-18 杭州汇萃智能科技有限公司 一种物体圆度测量方法
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US11184739B1 (en) 2020-06-19 2021-11-23 Honeywel International Inc. Using smart occupancy detection and control in buildings to reduce disease transmission
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CN114549403A (zh) * 2022-01-07 2022-05-27 中国地质大学（武汉） 一种机械零部件侧剖面多单体智能精密几何圆心检测方法
US11372383B1 (en) 2021-02-26 2022-06-28 Honeywell International Inc. Healthy building dashboard facilitated by hierarchical model of building control assets
US11402113B2 (en) 2020-08-04 2022-08-02 Honeywell International Inc. Methods and systems for evaluating energy conservation and guest satisfaction in hotels
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CN114549403B (zh) * 2022-01-07 2024-05-14 中国地质大学（武汉） 一种机械零部件侧剖面多单体智能精密几何圆心检测方法

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US11726516B2 (en) 2015-08-05 2023-08-15 Lutron Technology Company Llc Load control system responsive to the location of an occupant and/or mobile device
US10599174B2 (en) 2015-08-05 2020-03-24 Lutron Technology Company Llc Load control system responsive to the location of an occupant and/or mobile device
CN108474583A (zh) * 2016-03-22 2018-08-31 日立江森自控空调有限公司 空调机
JP2017172828A (ja) * 2016-03-22 2017-09-28 日立ジョンソンコントロールズ空調株式会社 空気調和機
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US11626004B2 (en) 2018-09-05 2023-04-11 Honeywell International, Inc. Methods and systems for improving infection control in a facility
US11288945B2 (en) 2018-09-05 2022-03-29 Honeywell International Inc. Methods and systems for improving infection control in a facility
US11887722B2 (en) 2019-01-11 2024-01-30 Honeywell International Inc. Methods and systems for improving infection control in a building
US10978199B2 (en) 2019-01-11 2021-04-13 Honeywell International Inc. Methods and systems for improving infection control in a building
CN110349199B (zh) * 2019-06-25 2021-07-30 杭州汇萃智能科技有限公司 一种物体圆度测量方法
CN110349199A (zh) * 2019-06-25 2019-10-18 杭州汇萃智能科技有限公司 一种物体圆度测量方法
US11620594B2 (en) 2020-06-12 2023-04-04 Honeywell International Inc. Space utilization patterns for building optimization
US11914336B2 (en) 2020-06-15 2024-02-27 Honeywell International Inc. Platform agnostic systems and methods for building management systems
US11783652B2 (en) 2020-06-15 2023-10-10 Honeywell International Inc. Occupant health monitoring for buildings
US11783658B2 (en) 2020-06-15 2023-10-10 Honeywell International Inc. Methods and systems for maintaining a healthy building
US11184739B1 (en) 2020-06-19 2021-11-23 Honeywel International Inc. Using smart occupancy detection and control in buildings to reduce disease transmission
US11778423B2 (en) 2020-06-19 2023-10-03 Honeywell International Inc. Using smart occupancy detection and control in buildings to reduce disease transmission
US11823295B2 (en) 2020-06-19 2023-11-21 Honeywell International, Inc. Systems and methods for reducing risk of pathogen exposure within a space
US11619414B2 (en) 2020-07-07 2023-04-04 Honeywell International Inc. System to profile, measure, enable and monitor building air quality
US11402113B2 (en) 2020-08-04 2022-08-02 Honeywell International Inc. Methods and systems for evaluating energy conservation and guest satisfaction in hotels
US11894145B2 (en) 2020-09-30 2024-02-06 Honeywell International Inc. Dashboard for tracking healthy building performance
US11662115B2 (en) 2021-02-26 2023-05-30 Honeywell International Inc. Hierarchy model builder for building a hierarchical model of control assets
US11599075B2 (en) 2021-02-26 2023-03-07 Honeywell International Inc. Healthy building dashboard facilitated by hierarchical model of building control assets
US11815865B2 (en) 2021-02-26 2023-11-14 Honeywell International, Inc. Healthy building dashboard facilitated by hierarchical model of building control assets
US11372383B1 (en) 2021-02-26 2022-06-28 Honeywell International Inc. Healthy building dashboard facilitated by hierarchical model of building control assets
US11474489B1 (en) 2021-03-29 2022-10-18 Honeywell International Inc. Methods and systems for improving building performance
CN114549403A (zh) * 2022-01-07 2022-05-27 中国地质大学（武汉） 一种机械零部件侧剖面多单体智能精密几何圆心检测方法
CN114549403B (zh) * 2022-01-07 2024-05-14 中国地质大学（武汉） 一种机械零部件侧剖面多单体智能精密几何圆心检测方法

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