WO2012141663A1 - Procédé de suivi individuel d'objets multiples - Google Patents

Procédé de suivi individuel d'objets multiples Download PDF

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WO2012141663A1
WO2012141663A1 PCT/TR2011/000082 TR2011000082W WO2012141663A1 WO 2012141663 A1 WO2012141663 A1 WO 2012141663A1 TR 2011000082 W TR2011000082 W TR 2011000082W WO 2012141663 A1 WO2012141663 A1 WO 2012141663A1
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objects
living
nonliving
tracking
image
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Alptekin Temizel
Cigdem BEYAN
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Definitions

  • the present invention relates to a tracking method, which is capable of individual tracking of multiple objects (such as people and their belongings) and an abandoned object detection method is proposed based on individual tracking of objects.
  • US2004120581A1 numbered patent application can be shown as an alternative method to eliminate the false alarms. It uses a motion pattern database to analyze motions of inside the movie.
  • CA2640931A1, RU2368952, US2004120581A1,US2009010493A1, US2010266159A1 and WO2008078112A1 are numbers of related patent applications, but they are not considered to be of particular relevance to this invention. Brief Description of the Invention
  • Figure 1 - is block diagram of the proposed tracking method's object discrimination and object tracking parts.
  • Figure 2 a - is visible band Image
  • Figure 3a - is an unprocessed thermal image.
  • Figure 3b - is a segmentation results for image of 3a.
  • Figure 4a - is an example of visible band image.
  • Figure 4b - is a thermal form of an image 4a.
  • Figure 4c - is an image 4a of object discrimination result with error.
  • Figure 4d - is segmented living object of image 4a.
  • Figure 4e - is segmented living non-object of image 4a.
  • Figure 5 - is Improved, Adaptive Mean Shift Tracking method.
  • Figure 6 (n-1) - is non-occlusion detected form of analyzed image.
  • Figure 6 (n) - is occlusion detected form of analyzed image.
  • Figure 7 - is the correspondence based object matching after re-initialization of trackers.
  • Figure 8 Frame # 76 - is an example of association of objects with their owners.
  • Figure 8 Frame # 82 - is an example of association of objects with their owners.
  • Figure 8 Frame # 95 - is an example of association of objects with their owners.
  • Figure 8 Frame # 106 - is an example of association of objects with their owners.
  • Figure 8 Frame # 109 - is an example of association of objects with their owners.
  • Figure 8 Frame # 125 - is an example of association of objects with their owners.
  • Figure 9 Frame # 168 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 199 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 334 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 415 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 427 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 475 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 503 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 531 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 544 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 556 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 562 - is an example of detection of an abandoned item.
  • Figure 9 Frame # 589 - is an example of detection of an abandoned item.
  • the inventive tracking method comprises two main image-analyzing groups of steps.
  • the aim of the first group of steps is discrimination of living and non living objects.
  • the second group of steps is related with object tracking. Discrimination starts with background subtraction (101), which is applied to the visible band image. Then, connected component analysis is utilized to remove the noise (102). On the other hand, local intensity operation is applied to thermal image (103) and the result of this operation is post-processed to complete (104) and close possible holes which might be formed after local intensity operation.
  • the second group of object discrimination (105) step is the fusion step which uses both modalities.
  • a rule based method and connected component analysis are used to extract objects and classify them as living or nonliving.
  • each object (living and/or nonliving) is tracked using our improved, adaptive mean shift tracking algorithm (106). While tracking objects, living and nonliving objects are also associated with each other and owner/carried object relation is set for tracked objects (107). Abandoning of an object was detected by using these relations and tracking the objects separately (108).
  • x (t is the value of pixel at time t
  • BG is the background
  • FG is the foreground
  • pi ⁇ 2 , MM and Oi
  • ⁇ 2 ⁇ ⁇ are the estimates of mean and variance for the Gaussian components respectively.
  • ⁇ , n 2 , , n M are the weight values that are nonnegative and summation is equal to 1.
  • Equations (2), (3) and (4) show how Gaussian model parameters are being updated.
  • m is set to one if its "close” component to largest n m and the others are set to 0.
  • New sample is "close” to component if the Mahalanobis distance between them is less than four standard deviations. Square distance from mth component can be calculated by using Eq. (5). If the new sample is close to the component, the new sample belongs to 99% confidence level and can be determined as a part of foreground.
  • background subtraction is applied to extract a stationary background image.
  • Any background subtraction algorithm can be used in this stage, as long as the stationary background image allows discrimination of foreground objects.
  • D N/A rect (6)
  • D is the density of object
  • N is the number of pixels that object has
  • a rec t is the area of the bounding rectangle.
  • each connected component is classified as noise and removed from the image if its density is smaller than the density threshold and the number of pixels that belongs to this object is smaller than the maximum number of pixel threshold.
  • An example result of background subtraction and noise removal step is shown in Figure 2.
  • thermal domain images are constructed from energy emitted by objects and living objects emit more energy compared to nonliving objects, pixels of living objects appear brighter than pixels of nonliving objects (in white-hot setting).
  • the invention uses local intensity operation (LIO) (R. Heriansyah and S.A.R. Abu-Bakar, "Defect detection in thermal image for nondestructive evaluation of petrochemical equipments", NDT & E International, Vol. 42, Issue 8, pp. 729-740, Dec. 2009.) for defect detection in thermal images. We also utilized this operator, which brightens the bright pixels and darkens the dark pixels, in a similar fashion to segment pixels belonging to living objects.
  • LIO local intensity operation
  • I(x,y) is given as a pixel in thermal image written as z 0 , and neighbors of it I(x-l,y-l), I(x-l,y), I(x-l,y+l), I(x,y-1), I(x,y+1) , I(x+l,y-l), I(x+l,y), I(x+l,y+l) are written as z z 2 , z 3 , z 4 , z s , z 5/ z 7 , z 8 respectively. Then, Z will be product of the neighboring pixels:
  • a new image is created according to Z for each pixel in thermal image by defining intensity brightness operation by using Eq. (8).
  • g(x, y) Z (8)
  • g(x, y) is the pixel value at (x, y) of new image.
  • these image pixels are normalized to gray-scale range.
  • the normalization process is done by dividing these pixels into the maximum pixel value within the image.
  • MAT Mean Absolute Thresholding
  • T round ⁇ J where T is the threshold value, I max is the maximum pixel value, I min is the minimum pixel value.
  • hot objects such as heating systems, radiators or any nonliving objects which are hotter than the environment are also captured brighter than the other objects and segmented as a result of this process.
  • our method does not discriminate these objects as people and hence, false alarms due to stationary hot objects are prevented.
  • the algorithm above may not find the object precisely and some gaps may be observed on the object's body due to the clothing. These problems are rectified with postprocessing. To make these objects single piece, it is needed to complete and close these holes in binary images by using some morphological operations. First, objects in binary images (result of local intensity operation, Figure 3b) are completed by hole-filling. Then, these binary objects are closed.
  • the object discrimination step is the fusion step that both thermal and visible band images are used. It is the main step for individual tracking of objects such as people and their belongings.
  • the objects coming from result of background subtraction and noise removal (a binary image) in visible data and the objects coming from result of local intensity operation and post processing (a binary image) in thermal data are utilized.
  • the mean shift tracking method is an optimization algorithm based on object representation ⁇ . Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking", IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, pp. 564-577, May 2003). It is an iterative scheme, uses a nonparametric kernel and executes until the goal is attained. It basically tries to find an object in the next image frame which is most similar and close to the initialized object (object model) in the current frame. It compares the histogram of the object model and histogram of the candidate object in the next frame. The aim is to maximize the similarity between the two histograms.
  • k is the kernel function which gives more weight to the pixels at the centre of the model
  • C is a normalizing constant which provides that sum of the histogram elements is 1
  • u represents histogram bin
  • n is the number of pixels in the object model.
  • 8 is the Kronecker delta function and b represents histogram binning function for pixels at location
  • a candidate model is constructed. Similar to the target model's pdf, the candidate model's pdf at location y is given by h ⁇ ⁇ ⁇ - (i2) where h is the kernel size which provides the size of the candidate objects.
  • Object detection is the first step for object tracking and this could be either manual or automatic.
  • many studies assume that the objects which will be tracked are selected manually by an operator. However, if manual initialization is used since new objects could not be tracked when they enter the scene after the initialization frame, it is expected that the operator regularly selects all the new objects, which prevents system to be automatic.
  • automatic initialization any new object entering the scene could to be tracked without any need for a human operator.
  • a fully automatic system is proposed and for initialization of the objects' bounding boxes results of the object discrimination step are used and for each nonliving or living object a tracker is defined. Additionally, these bounding boxes are used as a mask to decrease the search area of the mean shift tracker. This increases the proposed system's tracking accuracy and performance. Due to the fact that it reduces the search area of the frame, the required number of iterations to find the new position of object model is decreased.
  • trackers To handle the changes in size or shape, we update trackers every 25 frames. To detect new objects as well as objects that leave the scene, numbers of objects in adjacent frames are compared and if those numbers are not equal then trackers are updated. To handle occlusion and split and to detect newly emerging objects; the locations of bounding box of each object are compared. If an intersection exits then trackers are refreshed to handle the inclusion of front objects color. Separate trackers are initialized for each living and nonliving object and the same algorithm is used independent of the object type.
  • Closeness is defined as the distance between the centre of mass of these two objects ( ⁇ ⁇ and o p ) and Euclidean formula (Eq. 14) is used to calculate this distance. Similarity, on the other hand, is calculated by using the size ratio of the objects (Eq. 15). If distance between object o t and object o p is smaller than a distance threshold and the size ratio of objects o i and smaller than a size threshold, we define object o i as a corresponding object for o p .
  • closeness is a successful criterion since the displacement of an object between adjacent frames should be small. However, it is not a sufficient criterion since objects that are close to each other in previous frame may interfere and the matching might be incorrect. Therefore, similarity criterion is also required. Using similarity criterion is, on the other hand, useful as objects do not scale too many between consecutive frames.
  • O t could be a new object or it could have been occluded by another object.
  • O t could be a new object or it could have been occluded by another object.
  • color histograms of 0 and O t are stored in order to compare it when a split occurs and a tracker is created to follow the occlusion object ⁇ 3 ⁇ 4.
  • nonliving object is indexed with a notation (Owner Index.Object Index For The Owner).
  • the number before dot shows the nonliving object's owner's index and the number after the dot shows the index of nonliving object.
  • 1.1 is the object belonging to person 1
  • 2.1 is the object belonging to person 2.
  • Abandoned object detection is the main aim of this invention. Integration of the abandoned object detection into the tracking system may allow the person who leave luggage unattended to be tracked and detected. This method is successful to identify the owner of the abandoned object if a person who leave the luggage will stay near it until it is detected as an abandoned object. However, when the luggage is left and the owner of the luggage exits from the field of view, it wouldn't be possible to find the owner without making some extensions to the system. Therefore, association of living and nonliving objects is essential and necessary as it allows finding the owner of unattended luggage.
  • the nonliving object is detected as an abandoned object when its owner leaves the field of view and the alarm is set off after N frames passed. The alarm is removed immediately when the nonliving object is removed. To prevent false alarms in the case of merging of objects, the object's owner is checked whether it is occluded and formed a new object or not. ( Figure 9).
  • Figure 9 and its frames involve Person 3 leaving her backpack (object 3.1) on the floor. After it is detected as an abandoned item, temporary occlusions because of moving persons 5 and 6 do not cause the system to fail. The alarm is raised (Frame #556) after the person owning the backpack leaves.
  • thermal and visible band cameras To track living and nonliving objects and detect abandoned nonliving objects, firstly, images captured from thermal and visible cameras are registered. Both thermal and visible band cameras should be adjusted properly to capture a similar field of view. However, it is not practically possible to capture exactly the same field of view (FOV) for both thermal and visible band cameras since these cameras have different parameters (such as different sensor types and lenses). Therefore, a crop operation is performed for both thermal and visible band frames to set almost same FOV for both thermal and visible images. Then, homography is performed manually by selecting reference points in both thermal and visible domain for the image registration. To find corresponding pixels of each pixel, homography matrix is constructed. To obtain homography matrix Eq. (16) and (17) and reference points selected from both thermal and visible images are used.
  • H V ref x T ⁇ ⁇ (17)
  • v ref is the reference point matrix for visible domain
  • T ref is the reference point matrix for thermal domain
  • H is the homography matrix for registration. The more pixels are selected, the better registration results can be obtained. In this invention, 20 reference points are selected for each dataset. Once the capture and homography parameters are obtained, these parameters can be used without changing as long as the camera positions are not changed.

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Abstract

La présente invention porte sur un procédé de suivi qui est capable de suivre individuellement de multiples objets (tels que personnes et objets leur appartenant) et un procédé de détection d'objet abandonné. De multiples objets sont suivis au moyen du procédé de l'invention. En plus d'une bande visible, des images thermiques sont également utilisées et ces deux modalités sont fusionnées pour suivre des personnes et des objets qu'elles transportent séparément au moyen de leurs signatures thermiques. En utilisant les informations provenant de différentes modalités, des trajectoires des objets sont trouvées, des informations de propriété pour un objet non vivant sont déterminées et des objets abandonnés sont détectés. De meilleures performances de suivi sont également obtenues en comparaison de l'utilisation d'une modalité unique. Nous utilisons une modélisation d'arrière-plan adaptative et une opération d'intensité locale en association avec un suivi à décalage moyen pour un suivi entièrement automatique. Des dispositifs de suivi sont rafraîchis afin de résoudre les problèmes éventuels qui peuvent se produire à la suite de modifications de la taille et de la forme de l'objet et afin de traiter l'occlusion et la division, et afin de détecter des objets nouvellement apparues ainsi que des objets qui sortent de la scène.
PCT/TR2011/000082 2011-04-13 2011-04-13 Procédé de suivi individuel d'objets multiples Ceased WO2012141663A1 (fr)

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WO2017027212A1 (fr) * 2015-08-13 2017-02-16 Microsoft Technology Licensing, Llc Système de suivi de caractéristique de vision par ordinateur
CN109460077A (zh) * 2018-11-19 2019-03-12 深圳博为教育科技有限公司 一种自动跟踪方法、自动跟踪设备及自动跟踪系统
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CN115497056A (zh) * 2022-11-21 2022-12-20 南京华苏科技有限公司 基于深度学习的区域内遗失物品检测方法
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WO2017027212A1 (fr) * 2015-08-13 2017-02-16 Microsoft Technology Licensing, Llc Système de suivi de caractéristique de vision par ordinateur
CN106469443A (zh) * 2015-08-13 2017-03-01 微软技术许可有限责任公司 机器视觉特征跟踪系统
CN106469443B (zh) * 2015-08-13 2020-01-21 微软技术许可有限责任公司 机器视觉特征跟踪系统
CN105825525A (zh) * 2016-03-16 2016-08-03 中山大学 一种基于Mean-shift模型优化的TLD目标跟踪方法及其装置
US10558886B2 (en) 2017-11-15 2020-02-11 International Business Machines Corporation Template fusion system and method
CN109460077A (zh) * 2018-11-19 2019-03-12 深圳博为教育科技有限公司 一种自动跟踪方法、自动跟踪设备及自动跟踪系统
CN109460077B (zh) * 2018-11-19 2022-05-17 深圳博为教育科技有限公司 一种自动跟踪方法、自动跟踪设备及自动跟踪系统
CN111797727A (zh) * 2020-06-18 2020-10-20 浙江大华技术股份有限公司 一种检测路面抛洒物的方法、装置及存储介质
CN111797727B (zh) * 2020-06-18 2023-04-07 浙江大华技术股份有限公司 一种检测路面抛洒物的方法、装置及存储介质
CN111913435A (zh) * 2020-07-30 2020-11-10 浙江科技学院 一种基于堆积沙漏网络的单/多目标关键点定位方法
EP4250217A1 (fr) * 2022-03-22 2023-09-27 Fujifilm Business Innovation Corp. Appareil de traitement d'informations, programme et procédé de traitement d'informations
CN115497056A (zh) * 2022-11-21 2022-12-20 南京华苏科技有限公司 基于深度学习的区域内遗失物品检测方法

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