Person Detection, Reidentification. Using Spatio-Color-based Model for Non-Overlapping Multi-Camera Surveillance Systems

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1 Smart Computing Review, vol. 2, no., February Smart Computing Review Person Detection, Reidentification and Tracking Using Spatio-Color-based Model for Non-Overlapping Multi-Camera Surveillance Systems Farah Jahan, Mohammad Khairul Islam, and Joong-Hwan Baek 2 Department of Computer Science and Engineering, University of Chittagong / Chittagong-433, Bangladesh / {farah_csc, mkicubd}@yahoo.com 2 Department of Information and Telecommunication Engineering, Korea Aerospace University/ Goyang-city, Gyeonggi-do, 42-79, South Korea / jhbaek@kau.ac.kr * Corresponding Author: Farah Jahan Received November 23, 20; Revised January 2, 202; Accepted January 28, 202; Published February 29, 202 Abstract: The main goal of smart video surveillance is to develop an intelligent system in order to provide security to people and sensitive areas. Both person detection and tracking are challenging and crucial problems in intelligent video surveillance systems because a person's body changes its shape while moving. Multiple camera-based visual surveillance systems can be extremely helpful in expanding a surveillance area and avoiding occlusion. To monitor a wide area of interest, reidentification of persons across multiple disjointed fields of view is crucial. In this paper we propose a novel method for detecting, re-identifying and tracking persons as they move between different disjointed camera views. We use color thresholding to detect the presence of moving objects and validate them for personhood by analyzing shape information. We generate a discriminative signature for each person based on their spatio-color appearance information in order to re-identify them. The spatio-temporal information as well as labels obtained by our reidentification method helps the system to keep track of persons. Our experiment demonstrates that the proposed approach works in real-time and demonstrates excellent detection, re-identification and tracking accuracy. DOI: /smartcr

2 Smart Computing Review, vol. 2, no., February Keywords: Video surveillance, person detection, re-identification, tracking, spatio-color, spatio-temporal. Introduction V isual surveillance of a dynamic scene with multiple cameras can be used to detect, recognize, and track certain objects from image sequences, and more importantly to understand and describe object behaviors associated with the video. The main goal of visual surveillance is to develop intelligent visual surveillance to replace traditional passive video surveillance. An intelligent video surveillance system is defined as a system that thinks like a person, and therefore is an application of smart computing. Systems for observing and tracking persons through non-overlapping camera views are becoming important in the area of surveillance. We propose a system that is able to detect persons and track them continuously within the coverage of the camera view. When the persons move out of the coverage view of a camera we extrapolate disconnected tracks. We also introduce a signature-generation process of each subject based on appearance. Thus we can link disconnected tracks of the same subject observed by non-overlapping camera views. This process is called person re-identification as it identifies the same person again that has previously been observed by one or more different cameras. In Section 2, we review the previous work in person detection, re-identification, and tracking. Section 3 briefly describes our proposed method. We detail our person detection, re-identification, and tracking methods in Sections 4, 5 and 6. Section 7 presents the experimental results, and finally we conclude our work in Section 8. Related Work In the last few years, two main approaches to person detection have been explored. The first approach consists of a generative process where detected parts of the body are combined according to a known person model. The second approach considers purely statistical analysis that combines a set of low-level features within a detection window to classify the window as one containing a person or not []. There is extensive literature on person detection [2, 3, 4, 5]. Dalal and Triggs [6] propose Histograms of Oriented Gradient (HOG) for person detection and obtain good results with multiple datasets. The HOG features point out the spatial distribution of edge orientations. HOG is a very useful method to detect persons in still images. However, using HOG in a video stream is very time consuming, because here we can easily locate moving objects. Many applications require the ability to re-identify an individual across multiple disjointed fields of view. Therefore, after person detection, we extract a feature from the detected person silhouette area. This process is called signature generation. Among the existing approaches, some use passive biometrics such as face [7] and gait [8]. Here, we focus on re-identification algorithms that rely on the overall appearance (clothing) of the individual. Several approaches have been proposed where invariant signatures are generated based on the global appearance of an individual for tracking through disjointed views of multiple cameras [7, 9, 0,, 2, 3, 4, 5, 6]. Our appearance-based signature generation process is very fast, and it is also discriminative by both color and spatial location. Gheissari et al. [8] propose a temporal signature which is invariant to the position of the body and the dynamic appearance of clothing within a video shot. But this method considers only the frontal view of a person's body. Our re-identification method outperforms them [8, 2, 4, and 6]. Person tracking is very challenging because a person's body shape is not fixed; it changes every moment. Appearance can also be changed by illumination or other factors. Javed et al. [9] demonstrate the tracking of persons in nonoverlapping camera views. Bodor et al. [20] propose a system to track persons to detect suspicious motions/activities. Wang et al. [2] and Rahimi et al. [22] also implement tracking for a monitoring system. Our tracking method uses very simple heuristics which make it is faster to track persons within different non-overlapping camera views. Proposed Method Our research consists of three major areas. The first area is simply person detection. The second is discriminative signature generation from individuals based on appearances and identification. The third area is tracking individuals within the same camera view where they are first detected, and also when they re-enter disjointed camera views. Figure below shows a

3 44 Jahan et al.: Person Detection, Re-identification and Tracking Using Spatio-Color-based Model schematic overview of the system components. The shaded blocks indicate our contributions in person detection, reidentification, and tracking. At first, we detect moving object(s) from a video by comparing the current frame with a background model, and check the aspect ratio and shape complexity to validate person blobs. Since people stand/walk vertically, we apply some projection methods to precisely locate individual blobs. After detecting a person successfully, we generate a signature for individual persons using color information and the spatial relationship of those colors. This signature is used to identify the person within the same camera view (where the person is first observed) and also other cameras with disjointed views. We apply a fast-tracking method which is done by detecting overlapping blob areas in current and previous frames and the similarity of color that they share. We give each person an identity (ID) and track them as they move within the coverage of the camera view. Person Detection We proposed the Shape-based Person Detection (SBPD) method to detect moving persons in a video stream. Figure 2 shows the main steps of SPBD. Adaptive Background w x h Suppose a video stream consists of n frames, each frame having a resolution of. We construct a background image with the same resolution where each pixel has its own model. From the stream, we get n intensity values where each intensity can be defined as a point in a 3D spatio-temporal space I(x,y,t) where (x, is the spatial position in frame t where t {,2,.., n}. Thus, for a background pixel at position ( x, the n values are modeled using their color information in the time domain. In this paper, we modeled a background pixel using its median in the time domain. We denote a pixel in the background model by B ( x, where ( x, is the position of the pixel in the image plane. So, the model can be defined as Equation (): B( x, median( I( x, y, t) t {,2,..., n}) () We construct a background model for each camera view. Since each pixel in the model is the median of the possible state of the pixel, it reflects the highest probable state of the pixel. Thus it can properly address both simple and difficult background scenarios as depicted in Figure 5. Since the intensity of the background changes over time due to illumination changes in the total environment, we need to update the previous background after a particular time stamp. We name this background up-gradation process adaptive background. For instance, if we consider a background at time t as B(t) and B( t g) at time t g, we find the total I threshold illumination changes using Eq. (A.) in Appendix A. If in Eq. (A.), we update the background using Eq. (). In this research, we construct one background model for each camera.

4 Smart Computing Review, vol. 2, no., February A smoothing process simply slides a filter mask to every pixel of an image. The filtering mask and filtering process are shown in Eq. (A.2) and (A.3). Foreground Detection Given an input frame from a capturing device, the foreground blob detection process filters out the background from the frame and keeps the moving blobs. In this case, it is assumed that when a moving object enters a camera view, the region in the frame occupied by the object experiences an abrupt change with respect to the corresponding background region. Suppose that the average change in pixel intensity is greater than some threshold value th, and we denote the intensity of a pixel at location ( x, both in the background and input frame by B ( x, and I ( x, respectively. Then our blob detection process proceeds as shown in Eq. (A.4). Thus, a binary image D is obtained from Eq. (2). Morphological Processing if I( x, B( x, th D( x, (2) 0 else In D ( x, there might be some false positive response due to noise. To avoid any potential noise effect, we apply morphological operations, e.g. erosion and dilation, on D ( x,. Erosion operations remove false alarms due to noise. The dilation process, on the other hand, removes false negatives by filling in the holes. The equations of both erosion and dilation are mentioned in Appendix A.

5 46 Jahan et al.: Person Detection, Re-identification and Tracking Using Spatio-Color-based Model Blob Localization A moving person always walks or runs, keeping the body perpendicular to the ground. Therefore, we first separate the blob areas along the horizontal axis before doing the vertical axis. For horizontal separation, we project the image pixel counts onto the horizontal axis. Let x and y denote the horizontal and vertical positions of a pixel in the binary image. Then the horizontal or x - projection of the image at x -column denoted by p(x) can be defined using Eq. (3): p( x) h y S( x, (3) where h is the height of the binary image S and S( x, is the value of either 0 or at position ( x,.we calculate a projection vector X { p( x) x w} where w is the width of image S. Naturally, there is always a horizontal gap between two blobs. We draw the boundaries of each blob by selecting the start and end points of a blob in the x -projected vector. A blob starts at some column i in the X if X ( i ) 0 and X ( i) 0 where i { 2,..., w}. On the contrary, the blob ends at j if X ( j) 0 and X ( j ) 0 where j { i, i,..., w } and the width of the blob denoted by wb is defined as: w b j i. We crop the selected blob region from the image by selecting the region ( i : j) (: h) where (:) denotes range. To select the top and bottom boundaries of the blob we apply y -projection or vertical projection on the cropped area. The y -projection at a row, denoted by p (, can be defined as Eq. (4): where w b is the blob width. w b p x S( x, ( (4) Let us denote the y -projection by a vector Y { p( y h} where h is the height of the image. The top boundary of a blob in the cropped region is some value u if u satisfies Y ( u ) 0, and Y ( u) 0 where u { 2,..., h}.the bottom is some value v if it satisfies Y ( v) 0, and Y ( v ) 0, where { u, u,..., h } h is defined as v u. We calculate the area of a v and the height of the blob, denoted by b blob, denoted by A, as in Eq. (5): l w b th b A S x, xl yt h b ( (5) where l and t are the left and top borders, and w b and h b are the width and height of a blob in a binary image. Figure 3 shows an example of the blob-based localization method. Person Blob Selection Naturally, all contiguous blobs are not persons. Some of them are trees and other noise sources. At this stage we calculate the aspect ratio and shape complexity to detect person silhouettes in the image. For a window of size we calculate the aspect ratio, denoted by w h, using Eq. (6). h a (6) w We find that the detector shows best performance in selecting persons when we set the aspect ratio in the range of. If any blob passes the aspect ratio test, we consider shape.5 a 3.5 i.e., a candidate must satisfy.5w h 3. 5w complexity as the second parameter. Let c be the contour length of a blob and A be its area. Then the shape complexity, denoted by S, can be calculated as Eq. (7): s c A (7)

6 Smart Computing Review, vol. 2, no., February In our experiment, after tuning the shape complexity, we find that the detector performs best at 0.05 s Finally, a person candidate that passes the aspect ratio and shape complexity tests is identified as a person. Figure 4 shows the detected person areas by bounding boxes created by applying our person detection process. Person Re-identification Preprocessing We perform histogram equalization to increase the overall contrast of images with persons in them. In this work, we split a silhouette into 3 color planes - R, G, and B planes. Then we apply histogram equalization to each plane and re-combine them to regenerate an illumination invariant silhouette. Figure 5 shows the histogram equalization process.

7 48 Jahan et al.: Person Detection, Re-identification and Tracking Using Spatio-Color-based Model Signature Generation We use a spatial relationship among pixels containing similar colors [2, 24] and color histograms to generate a robust and discriminative signature for each individual. A color histogram along with spatial information is discriminative because they capture not only the values of the pixels but also their spatial relationships. We describe our signature-generation process in the following sections. Color Histogram w h and pixel color intensity at position ) Let I be an image with a resolution ( x, y in the image plane is denoted by I ( x,. A color histogram of the image can be produced by counting the pixel occurrences in a bin b, denoted by n b, defined as Eq. (8). n b N k (8) kb Where if the kb th k process for a person silhouette area. Spatial-Covariance pixel falls into binb, otherwise 0. Figure 6 presents the color histogram generation A spatial-covariance represents the relationship between the color and spatial position of pixels. Figure 7 (a) shows a color histogram. The matrix below (Figure 7 (b)) shows the pixels' spatial locations which are counted as the color histogram. For example, shaded cells in the matrix indicate spatial locations of 3 pixels that fall into the st bin. kb

8 Smart Computing Review, vol. 2, no., February Mathematically, for each histogram bin, the spatial-covariance contains the spatial mean and covariance as defined in Eqs. (A.7) and (A.8), respectively. We calculate the spatial mean b and covariance matrix b matrix b for each bin of the color histogram. The covariance for each bin is a symmetric matrix. The entries of a symmetric matrix are redundant with respect to the main diagonal (top left to bottom right). If an entry at valid: th i row and th j column is written as a ij, then for all i and j, Eq. (9) is aij a ji (9) For our case, x is a 2-element vector, so we get 2 2 symmetric matrix for each bin. So, we consider the 2 values from the main and value from the non-diagonal. Combined Signature We use the R, G, and B planes for generating separate color frequency histograms and spatial-covariances. If we generate a n -bin histogram for each color plane and a 3-element vector as a spatial-covariance for each bin, we obtain a 4-element vector for each bin. Thus a color plane contributes n4 values to a signature. In our experiment, n is set to 8 which results in a 8x4x3 or 96-valued signature. We divide the silhouette into two equal parts, e.g. upper part of the body and lower part of the body, and obtain a 296 or 92-length signature. We show a schematic example of our proposed signature in Eqs.A.9.

9 50 Jahan et al.: Person Detection, Re-identification and Tracking Using Spatio-Color-based Model Our proposed signature generation method is more distinctive than just an RGB color histogram, because in RGB only color occurrences are counted to generate the signature but no positional information is considered. For example, the signatures of two persons are very different when one is wearing a red shirt and white pants while the other is wearing a white shirt and red pants, even though the RGB histograms are similar. In a spatially-enhanced signature the differences between those two people would be easy to spot. The performance of a classification system is dependent on the classifier. In our case, we use a regression tree as a classifier. PERSON TRACKING The identification module labels the person blog with one of a set of person IDs. Once a person blob is labeled, we start tracking that blob in the following frames as long as it is in camera view. In our system, we send the tracked blob into identification until we are sure of the identification label. For example, a blob can be classified in 50 consecutive frames. We believe that the majority of the classification results would be perfect even if the efficiency of the classification module was just above 50%. After that, we do not send the blob for further identification. Rather, we just label it using a previous statistical result. Figure 8 displays a detailed flow chart of our person-tracking algorithm. The following section describes the key content of the tracking algorithm. Overlapping Area If a blob is detected in the current frame, we discover it in the previous frame, if it exists, by calculating the overlapping area between the blob in the previous frame and the one in the current frame. To find an overlapping blob let us consider a blob B prev in the previous frame and B curr in the current frame. Let L prev, R prev, T prev and D prev be the left, right, top, and bottom boundaries of B prev, and let L curr, R curr, T curr, and D curr be the left, right, top, and bottom boundaries of B. Two blobs overlap if OA : min( area in B, area in ) 0. 5, where OA is the overlapped area of curr prev B curr B prev and B curr. The procedure to detect OA is illustrated in the pseudo-code of Figure 9. When multiple objects are nearby and they overlap with each other both spatially and temporally, we calculate the overlapped areas in the time domain. Then we consider the maximum overlapping blob as the replica of the blob in the previous frame.

10 Smart Computing Review, vol. 2, no., February Experimental Results We captured 7 video sequences in different places using a Sony Handycam HDR-CX2. We denoted each position as a camera view. The original resolution of the video frame is 280x720, but we downsampled it to 320x80 for fast computation. The application was executed on a PC with an Intel(R)Core(TM)2 Quad CPU with 4.0GB of RAM. We used Microsoft Visual C++ and the OpenCV 2.0 Library for developing our application. Our application can process 20 frames/second on average. We also evaluated our proposed method on a publicly-available set of videos. Person Detection Results Table shows a competitive performance of our person detection system and HOG. It is clear that our person detection algorithm is 57.5% faster than HOG. In this experiment, we used HOG and our proposed detector on the same person candidate blobs for a fair comparison.

11 52 Jahan et al.: Person Detection, Re-identification and Tracking Using Spatio-Color-based Model Precision and recall are two widely-used metrics for evaluating the correctness of a pattern recognition algorithm. In our case, we calculated the precision and recall using Eqs. (0) and (), respectively. TP Pr ecision (0) TP FP TP Re call () TP FN Where, TP, FP, and FN mean True Positive, False Positive, and False Negative respectively. True Negative Rate and Accuracy are calculated using Eqs. (2) and (3). Table 2 gives the overall person detection result for 7 camera views.

12 Smart Computing Review, vol. 2, no., February TN True negative rate TN FP (2) TP TN Accuracy TP TN FP FN (3) Where TN means True Negative. Figure0 shows a comparison between SBPD and HOG. Re-identification Results For re-identification, we tested our system to identify different persons, some of them on our campus and the rest from public datasets. We identified them in different situations using the same camera view, different camera views, and body poses. We measured the re-identification rate of our system using a Cumulative Match Characteristic (CMC) curve [2, 4]. For re-identification, we needed to generate a signature for each individual.

13 54 Jahan et al.: Person Detection, Re-identification and Tracking Using Spatio-Color-based Model Data Set I Figure displays a few example persons. Here, each row shows person areas captured from one viewpoint and each column shows two different views of a person captured by different cameras. In Figure 2, the CMC curve shows the performance of re-identification. Here persons detected in a camera view are used for training the classifier, and persons are identified in camera views different from that used for training. In this case, we applied a 2-fold cross validation process, where 50% of the samples are used for training and the remaining 50% are used for testing. We trained the classifier with images of 5 different persons shown in Figure. In Figure 2 we displayed comparative performances of signatures generated by color with spatial covariance and those generated only by color. Data Set II We conducted an experimental evaluation of our proposed method on a publicly-available series of videos ( homepages.inf.ed.ac.uk/rbf/caviardata/) showing persons recorded in corridors of a commercial mall. These videos (collected in the context of European project CAVIAR [ST ]) are of relatively low resolution, and include images of the same 6 persons seen by two cameras with very different viewpoints. Because it is a re-identification problem, we considered more than view. That is why, for detection and re-identification purposes, we limited out experiments to

14 Smart Computing Review, vol. 2, no., February those 6 persons. Figure 3 shows examples of person detection using our detector. Figure 4 shows the performance of reidentification on the detected persons from the CAVIAR data. Figs. 2 and 4 show that spatially-enhanced RGB signatures outperform simple RGB signatures. For example, the signatures of a person wearing a red shirt and white pants and a person wearing a white shirt and red pants are almost the same in the RGB signature method, but are quite different in spatially-enhanced RGB, because the position of the white and the red are different.figure 5 shows an overview of our re-identification scenario. Each image in this figure represents a video frame. The rectangles are the person detection results and the number over a rectangle is its ID. As one can see, the same person in different camera views has the same label; it means that the same person is tracked in different camera views.

15 56 Jahan et al.: Person Detection, Re-identification and Tracking Using Spatio-Color-based Model Conclusion In this paper we proposed an efficient algorithm for person detection, re-identification, and tracking. The detection was based on the quantization of color information followed by shape information. Re-identification is based on assigning an appearance-based signature of person(s), while tracking is performed with the help of re-identification and spatio-temporal information. For person detection, we checked a foreground blob using aspect ratio and shape complexity to determine whether or not it was a person. Experimental results confirm that the person detection method called SBPD outperforms the state-of-the-art HOG person detector. SBPD achieves 96% precision and has a 98.94% recall rate which are 6% and 43% higher than HOG, respectively. Our person detection method shows that it can correctly detect 96% of moving persons. For generating a person-based signature we use color intensity as well as spatial covariance of pixel locations. The correct reidentification rate is 96% and 93% on average in either the same or different camera views, respectively, which is also higher than a method that uses only a color histogram as the signature. Finally, we track moving persons by a fast overlapping blob detection method which uses spatio-temporal information and correlation of the color histograms of overlapped person blobs. Our tracking is scale and illumination invariant and its accuracy is 98%.The detection speed, reidentification rate, and tracking results are very satisfactory. We detected candidate blobs by first horizontal and second vertical projection after taking into consideration that a person walks keeping the body perpendicular to the ground. When a camera is rotated such that persons are not facing upright, we will need to apply geometric transformation before using our projection method. In future work, we will address person tracking when cameras are not in an upright position, and when people stay together to form one big blob from one camera view. References [] W. R. Schwartz, A. Kembhavi, D. Harwood, and L. S. Davis, Human detection using partial least squares analysis, in Proc. of IEEE 2th International Conference on Computer Vision (ICCV), pp.24-3, Article (CrossRef Link)

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17 58 Jahan et al.: Person Detection, Re-identification and Tracking Using Spatio-Color-based Model [23] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley Longman Publishing Co., Boston, MA, USA, 200. [24] S.T.Birchfield and S.Rangarajan, Spatiograms versus histograms for region-based tracking, in Proc. of the IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, pp.58-63, Appendix A Detecting illumination changes to update background Eq. (A.) is used. I w h w h B x, y, t) x y x y Linear filtering of an image f of size (A.3). ( B( x, y, t g) (A.) M N with a filter win, denoted as Eq. (A.2), the filtering process is done by Eq. win ( s t ) ( s, t) e (A. 2) 2 Blob detection process a b g x, win( s, t) f ( x s, y t) ( (A.3) satb B( x, th I( x, B( x, th The erosion of the binary image D by a structuring element returns an eroded image C. Let 2 Z be a 2-D integer space and D a binary image in e (A.4) 2 E in Z, denoted by D ()E, is defined by Eq. (A.5) which C D( ) E { Z ( E ) D } (A.5) e e Z The dilation on the eroded image C by a structuring element E, denoted byc returns a dilated image S, Spatial mean d 2 Z. Ed e, is defined by Eq. (A.6) which S C E { Z [( E ) C] C} (A.6) d d Z Covariance b N nb k x k kb (A.7) Where, b nb T b ( x k k b )( xk b ) n (A.8) b is the spatial mean, b is spatial covariance and x k ( x, is the spatial position of a pixel k. Schematic representation of signature for re-identification, l l l, l n,,,......, n, (A.9)

18 Smart Computing Review, vol. 2, no., February z z In the above signature, superscript z in n indicates bin index of histogram. ij bin z where i and j are row and column of the covariance matrix respectively. is an entry in covariance matrix of Farah Jahan received her Bachelor's of Science (Honors) in Computer Science & Engineering in December 2005 from the University of Chittagong, Bangladesh and a Master's of Engineering in Information and Telecommunication Engineering in August 20 from Korea Aerospace University, South Korea. She presently serves as an Assistant Professor in the Dept. of Computer Science and Engineering, University of Chittagong, Bangladesh. Her research areas are multimedia, image processing, and computer vision. Mohammad Khairul Islam received his Bachelor's of Science (Engineering) in Electronics & Computer Science in December 998 from Shahjalal University of Science & Technology, Bangladesh. He received his Master of Engineering and Doctor of Engineering in Information and Telecommunication Engineering in August 2007 and August 20 respectively from Korea Aerospace University in South Korea. He presently serves as an Associate Professor in the Dept. of Computer Science and Engineering, University of Chittagong, Bangladesh. His research areas are multimedia, image processing, and computer vision. Joong Hwan Baek received his Bachelor's of Science in Telecommunication Engineering in February 98 from Hankuk Aviation University, South Korea. He received his Master's of Science and Doctorate of Engineering in Electronics and Computer Engineering in July 987 and July 99 respectively from Oklahoma State University, USA. He presently serves as a Professor in the Dept. of Information and Telecommunication Engineering, Korea Aerospace University. His research interests include image processing, pattern recognition, and multimedia. Copyrights 202 KAIS