Motion Based Security Alarming System for Video Surveillance

Transcription

1 Motion Based Security Alarming System for Video Surveillance G. Raja Raman, M. Sarath Chandran, and S. R. Vinotha Abstract he protection of critical transportation assets and infrastructure is an importa topic these days. Robust detection of moving objects in video streams is a significa issue for security system by using video surveillance. In this paper, we propose a robust security alarming system based on detection of moving objects in video streams and an efficie security system. In this work, the alarming system will track the object motion from past N frames and will predict the object s coordinate in N+1th frame using boundary box descriptor and then apply Adaptive threshold based background subtraction model. After prediction the alarming module will ideify whether the object is eering, loitering, exiting io the secure area within the field of view of camera. he robustness of performance in real-time will be monitored using spatio-temporal analysis. Keywords Background subtraction model (BGS), Motion rajectory Estimation (ME), String Matching Procedure (SMP), Spatio-emporal Analysis (SA) I. INRODUCION here is an increasing demand for automatic methods for analyzing the vast quaities of surveillance video data generated coinuously by closed-circuit television (CCV) systems. One of the key objectives of deploying an automated visual surveillance system is to detect abnormal behavior patterns and moving objects. o achieve this objective, previously observed behavior patterns need to be analyzed, upon which a criterion on what is normal/abnormal is drawn and applied spatio-temporal analysis for moving objects. Due to the large amou of surveillance video data to be analyzed and the real-time nature of many surveillance applications, it is very desirable to have an automated system that runs in realtime and requires little human ierveion. In the area of surveillance, automated systems, to observe object movemes and detect dangerous action are becoming importa. he research challenge here is to quickly learn the permitted activities and set an alarm at any illegal or abnormal activity being performed. he effectiveness of a security alarming system shall be measured by 1) how accurately and robustly detect the moving objects in the video scene and 2) how well anomalies can be detected. o solve the problem, we develop a novel framework by adopting spatio-temporal analysis and string matching scheme. G. Raja Raman is with the Dhanalakshmi College of Engineering, Chennai, N INDIA (phone: ; rajaramandcecse@gmail.com). M. Sarath Chandran is with the Dhanalakshmi College of Engineering, Chennai, N INDIA ( sarath.chandran31@gmail.com). S. R. Vinotha is with the Computer Science and Engineering Departme, Dhanalakshmi College of Engineering, Chennai, N INDIA ( vinotharamaraj@gmail.com). Our framework consists of 1) Automatically segme a coinuous video sequence V io N video segmes. 2) Design the environme model by using few frames and to detect the target of the secure area using boundary box descriptor. 3) In foreground detection, to track the motion of the object in the video scene using adaptive threshold based background model. 4) In motion trajectory estimation, to measure trajectory of the motion objects using spatio-temporal method. First calculate the threshold value from object ceroid (X,Y) to target ceroid (Xt,Yt) and then ideify which object will be eer io secure area. 5) o recognize the normal behavior and anomaly detection uses string matching procedure. 6) If the object is eering io the secure area then the alarm will be on. here is a clear motivation to develop automated iellige vision-based monitoring systems that can aid a human user in the process of risk prediction and analysis. he rest of the paper is structured as follows: section 2 reviews related work to highlight the early coributions of this work. he proposed methodology is explained in section 3, data collection is briefed in section 4, expected output in section 5, and section 6 concludes the work. II. RELAED WORK Many researchers focus on the background subtraction model. Araki [22] has been suggested affine transform based motion estimation model and it was prone to error in background subtraction. Few papers can be found in the literature for foreground analysis [3], [17], [15]. hey analyzed the foreground as moving object, shadow, and ghost by combining the motion information. he computation cost is relatively expensive for real-time video surveillance systems because of the computation of optical flow. Mittal and Paragios [21] preseed a motion-based background subtraction by using adaptive kernel density estimation. In their method, optical flow is computed and utilized as a feature in a higher dimensional space. hey successfully handled the complex background, but the computation cost is relatively high. ao Xiang [1] deal with slow lighting changes, periodical motions from clutter background, slow moving objects, long term scene changes, and camera noises. But it cannot adapt to the quick lighting changes and cannot handle shadows well. he problem is holes appeared on the foreground mask for large homogeneous objects [20]. 6

2 More recely, the motion trajectory estimation proposed for either temporal or spatial information in the video sequence [4] [15] for multiple objects with similar motion, the tracking algorithm tends to fail. Anomaly trajectory analysis method based on single v-svm suggested [9] and limitation of this approach, no guaraee that this parameter will stay fixed, since the outlier ratio could change, thus is requiring a periodic modification of the ν parameter. Fatih Porikli etsuji Haga [12] work, usual eve is detected by using the affinity matrices and unusual eve is detected by conformity scores. But, main disadvaage of this approach is that they are all limited to the equal duration trajectories since they depend on the coordinate correspondences. Shaogang Gong et.al. [13] proposed recognition of group activities using Dynamic Probabilistic Networks (DPN). his model exploited the temporal relationships among a set of differe object temporal eves in the scene-level behavior ierpretation and group activities in a noisy outdoor scene is superior compared to that of a Multi-Observation Hidden Markov Model(MOHMM), a parallel Hidden Markov Model(PaHMM), and a Coupled Hidden Markov Model(CHMM). Kyoko [10] developed a method that can discriminate anomalous image sequences for more efficiely utilizing security videos and reduce the dimensionality of the data by PCA. o specify the kind of motion, a single frame of a sequence that coains motion is insufficie and uses the spatio-temporal feature obtained by extracting the areas of change from the videos. O.Boiman et.al [18] has developed a database of spatiotemporal patches for detecting anomaly from unseen video set. However, apart from the scalability problem, the approach in [18] has limitations in capturing the temporal ordering aspect of a behavior pattern due to the constrai on the size of the video patches. In particular, the approach can only detect unusual local spatiotemporal formations from a single objects rather than subtle abnormalities embedded in the temporal correlations among multiple objects that are not necessarily close to each other in space and time. Not that the anomaly detection method proposed in [19] was claimed to be online. Nevertheless, in [19], anomaly detection is performed only when the complete behavior pattern is observed. Limitation of this approach is less discriminative power. his problem is addressed by the approach proposed in [1], visual surveillance system is to detect anomaly behavior patterns using ruime accumulative anomaly measure and recognize the normal behavior using Likelihood Ratio est (LR) method and based on fully unsupervised behavior profiling and robust online anomaly detection and limitation is Less robust in dealing with instances of behavior that are not clear cut in an open-world scenario. Main problem of who is now eering the secure area under surveillance is of increasing importance for visual surveillance. Weiming Hu et.al.[15] uses secure area concept by recognition of face. From the detailed survey, it was revealed that the anomaly trajectory analysis involves more complexity due to timely alarming behavior. Hence, the proposed security alarming system aimed to use spatio-temporal method for protecting the secure area based on behavior of the objects in the video sequences. III. MEHODOLOGY he Proposed methodology describes spatio-temporal analysis to estimate the moving objects and string matching procedure for normal/anomaly detection. Fig. 1 Design steps of Security Alarming System We design the environmeal model by using few frames and to detect the target of the secure area. he required feature vectors are arget ceroid (Xt,Yt), op Left(L) and Bottom Right(BR) in the environme model, then the threshold value is calculated by using, Dis [( Xt, Yt) BR] = V ( 1) Fig. 2 Design of Environmeal Model he position of a poi may be expressed in imagespace or worldspace coordinates. he imagespace coordinates (or image position) refer to the pixel position of the poi on the video image, measured from the top left corner. he positive x-axis pois to the right and the positive y-axis pois down. Image units are like pixels except that they are doubles, not iegers. his means that the ceer of the top left pixel has coordinates; it is the top left corner of the top left pixel. he lower right corner of a 320x240 pixel image. he world space coordinates (or world position) refer to the scaled position of the poi relative to a specified world reference frame. he reference frame origin may be anywhere on or off the image and the positive x-axis may poi in any direction. he positive y-axis is 90 degrees couerclockwise from the positive x-axis. A. Foreground Detection and Object Descriptor First, Background subtraction model is adopted to detect foreground pixels by using adaptive threshold technique. Second, the foreground pixels in vicinity are grouped io a blob using the connected compone method. Each frame stores object descriptor in a scene eve. Each blob with an average PCH value greater than a threshold is then defined as a scene eve. A detected scene eve is represeed as a seven-dimensional (7D) feature vector f = [ x, y, h, w, R, M, M ] ( 2) f where (x,y)is the ceroid of the blob, (w, h) is the blob dimension, R f is the filling ratio of foreground pixels within px py 7

3 the bounding box associated with the blob, and (M px,m py ) are a pair of first-order momes of the blob represeed by PCH. Among these features, (x,y) are location features, (w,h) and R f are principally shape features but also coain some indirect motion information, and (M px,m py ) are motion features capturing the direction of object motion. Finally, the behavior pattern captured in the n th video segme v n is represeed as a feature vector p n, given as P = n [ P ] ( 3) n1 nn where n is the length of the n th video segme, and the tth eleme of P n is a K e -dimensional variable P = [ ] 1 k ke P ( 4) P corresponds to the t th image frame of V n, where p K is the posterior probability that an eve of the K th eve class has occurred in the frame. If an eve of the K th class is detected in the tth image frame of V n, 0<P K 1: otherwise,p K =0. Our eve based behavior represeation is illustrated in Fig.3. Fig. 3 Foreground Detection and Object Descriptor B. Motion rajectory Estimation In this work, motion-based recognition deals with the recognition of an object and/or its motion, based on motion in a series of images. In this approach, a sequence coaining a large number of frames is used to extract motion information. he advaage is that a longer sequence leads to recognition of higher level motions, like walking, running, which consist of a complex and coordinated series of eves. (a)secure area eering scenarios (b) Secure area leaving scenarios Fig. 4 Behavior patterns in a room eering/leaving scene. (a) Shows OBJ 1 is eering (E) io the secure area, OBJ 2 is trying to eering (R) io the secure area, OBJ 3 towards to the secure area. (b) Shows OBJ 1 is gone from the secure area, OBJ 2 is going away from the secure area and OBJ 3 is exiting from the secure area. Motion trajectory estimation is done based on spatio-temporal method. First, calculate the threshold value from object ceroid (x,y) to ceroid target (xt,yt) for eering/leaving. units (in pareheses). ABLE I SCENARIOS ESIMAION Eves Scenarios Anomaly detection Eering owards V(25) Dis[(x,y),(X t,y t ) rying V(15) Dis[(x,y),(X t,y t ) Eering V(5) Dis[(x,y),(X t,y t ) Leaving Gone V(25)< Dis[(x,y),(X t,y t ) Going away V(15)< Dis[(x,y),(X t,y t ) Exiting V(5)< Dis[(x,y),(X t,y t ) {V is threshold value, Dis is a Distance Metric using well known Euclidean distance} In Fig.4, for tracking one or two objects eering io the secure area, it is planned to calculate the threshold value for each objects separately by using, objectceroid Dis t arg etceroid ( x, y) ( x, y ) = V ( 5) Assume that there are two objects, then their hreshold Value1(V1) and hreshold Value2(V2) are compared. If V1 is less than V2 then allow the second object to eer io the secure area, after few seconds only. We define minimum threshold value between two objects. Eves Eering Leaving Loitering ABLE II MOION RAJECORY ESIMAION Anomaly Distance rate Detection rate (%) Dis[(x,y) and (x >50%(Negative),y )] decrease Dis[(x,y) and (x >50%(Positive),y )] increase ~50%(Negative and Positive) Dis[(x,y) and (x,y )] fluctuate Spatio-temporal motion based methods are able to better capture the information of gait motion. he advaage of our approach is low computational complexity and efficie in performance. However, we are susceptible to noise and to variations of the timings of movemes. C. Normal / Anomaly Detection he normal and anomaly (or suspicious) behaviors of human can be detected by using video sequences captured using a static camera in a video surveillance system. he Normal/anomaly behavior detection is based on evaluation of sequence of movemes exhibited using our proposed substring matching scheme. he behaviors can be analyzed by the scenarios estimated according to their sub sequences of occurrence given in the table.3. 8

4 Assume there are N frames for detecting anomaly behavior in the video scene. Based on the substring of N/2 frames, to recognize the behavior is normal or anomaly as detailed in table.3. ABLE III BEHAVIORAL ANALYSIS Eves Behavior detection Sequences If not, both objects will not be allowed to eer io the secure area. Alarming scenarios are illustrated in able.4, only three possible ways to eer io the secure area. If two objects are eering stage, will not possible in our approach. ABLE IV ALARMING SCENARIOS Object 1 Object 2 Alarming scenarios Eering Normal {,,, } {R, R,.R} {E, E, E, E} R R Not possible R E Alarm Leaving Normal {G, G, G G} {GA, GA,...GA} {EX, EX,...EX} E R Alarm E E Not possible Loitering Abnormal {, R, G, GA } Algorithm for String Matching: Input: N consecutive frames Output: Recognize normal/anomaly behavior Step 1: Consider N consecutive frames from video sequences. Step 2: Comparing the length of the substring for behavior analysis. (i) F= N frames E= {, R, E} L= {G, GA, EX} (ii) For all i(1 i N/2) do If F(i) to F(i+N/2) matches with anyone of the eve E scenarios or L scenarios exclusively as a substring of length N/2 then the human behavior is normal, otherwise the human behavior is abnormal. IV. DAA COLLECION In this section, we illustrate the effectiveness and robustness of our approach on security alarming system and online anomaly detection with experimes using datasets collected from video surveillance scenarios. A fixed CCV camera took coinuous recordings. A data set is collected over five differe days consisting of 6 hours of video, totaling to 432,000 frames captured at 20Hz with pixels per frame. his data set is then segmeed io sections separated by any motionless iervals lasting for more than 30 frames. V. EXPECED OUCOME In our approach, initially we design the environmeal model and then detected the target ceroid of the secure area. he following fig. 6 explains the expected outcome of security alarming system. Step 3: Return Label for the N frames. D. Alarming We propose robust security at the particular room. Compared to the existing technique, the alarming system cous the number of objects eering io the secure area without sensor. Alarm module ideifies which object is eering io the secure area. his module is used to avoid occlusion between objects. If one object is in the secure area means, after a few seconds only, the other object can eer io the secure area i.e. at a time only one object is allowed to eer io the secure area and also measure the similarity between target ceroid and object ceroid. Fig. 5 Alarming Scenarios We define minimum threshold value between two objects. Fig. 6 Sample Output VI. CONCLUSION In this paper, we have proposed a novel framework for a robust and effective security alarming system. Comparing the existing methods, Spatio-emporal analysis method is simple with low computational complexity. Motion based trajectory estimation is being done by calculating distance between object ceroid and target ceroid. his work is a serious effort to address the problem of anomaly detection in realistic scenarios. he proposed work can be useful in real time application such as AM couer, Bank, Z Categories and Research Organization. REFERENCES 9

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