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A Distributed Outdoor Video Surveillance System for Detection of Abnormal People Trajectories

A Distributed Outdoor Video Surveillance System for Detection of Abnormal People Trajectories. Simone Calderara , Rita Cucchiara , Andrea Prati Imagelab laboratory University of Modena and Reggio Emilia, Italy. Agenda. Motivations. Motivations

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A Distributed Outdoor Video Surveillance System for Detection of Abnormal People Trajectories

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  1. A Distributed Outdoor Video Surveillance System for Detection of Abnormal People Trajectories Simone Calderara, Rita Cucchiara, Andrea Prati Imagelablaboratory Universityof Modena and Reggio Emilia, Italy

  2. Agenda • Motivations • Motivations • The IMAGELAB distributed video surveillance system • Motion detection and tracking • Multi-camera consistentlabeling • Probabilistic people trajectoryclassification • Conclusions

  3. Motivations • Video surveillance: why? • Increasing security level of public places • Crime Prevention … • Why automated video surveillance? • Manual monitoring of large areas is difficult • Humans typically focus their attention on particular spots (some others may not be observed) • Interesting information may be extracted and stored for subsequent analysis (posterity logging for forensic off-line analysis)

  4. Motivations (2) • Distributed multiple cameras mean: • Wider coverage of the scene • Redundant data (improved accuracy) • Different viewpoints disambiguate groups, help with occlusions • Distributed multiple cameras - disadvantages: • System complexity can seriously rise with many cameras • Using multiple cameras is almost useless if data from different cameras are not correlated • Need for camera communication/coordination

  5. Motivations (3) • Previous generations of automatic surveillance systems are focused on robustly performing low-level tasks (motion detection, segmentation, perimeter control,…) • Next generations will need to operate at a higher level of abstraction (infer or learn behavioral patterns, detect specific behaviors, understand what is happening in the scene,…)

  6. Agenda • Motivations • The IMAGELAB distributed video surveillance system • Motivations • The IMAGELAB distributed video surveillance system • Motion detection and tracking • Multi-camera consistentlabeling • Probabilistic people trajectoryclassification • Conclusions

  7. Sakbot Ad hoc Hecol PPC IMAGELAB VS system Projects and prototypessince1998

  8. Park experimentation (LAICA Project Reggio Emilia)

  9. Agenda • Motivations • The IMAGELAB distributed video surveillance system • Motion detection and tracking • Motivations • The IMAGELAB distributed video surveillance system • Motion detection and tracking • Multi-camera consistentlabeling • Probabilistic people trajectoryclassification • Experiments • Conclusions

  10. Motion detection • SAKBOT based on temporal median + selective knowledge-based updating • Suitable modifications of SAKBOT [1] system to deal with outdoor requirements: • Bootstrapping: initial bkg model created using single difference at block-level (16x16) • Adaptive bkg differencing: hysteresis (local and pixel-varying) thresholding of the bkg difference [1] R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, “Detectingmoving objects, ghosts and shadows in video streams,” IEEE Trans. on PAMI, vol. 25, no. 10, pp. 1337–1342, Oct. 2003.

  11. Motion detection (2) • SAKBOT’s modifications (cont’d): • Fast “ghost” suppression: similar to bootstrapping (based on single difference), but at region-level: valid object only if a sufficient number of pixels are moving • Others: object validation, shadow suppression, …

  12. Objecttracking • Apperance-based tracking approaches keep track not only of the state but also of the shape at pixel-level, necessary for gait or posture analysis • AD-HOC (Appearance Driven Human tracking with Occlusion Classification) [2] based not only on object’s position and speed, but also on its appearance map and probability mask [2] R. Vezzani, C. Grana, R. Cucchiara, “AD-HOC: AppearanceDrivenHumantrackingwithOcclusionClassification”, under review in Pattern Recognition, 2007

  13. Agenda • Motivations • The IMAGELAB distributed video surveillance system • Motion detection and tracking • Multi-camera consistent labeling • Motivations • The IMAGELAB distributed video surveillance system • Motion detection and tracking • Multi-camera consistentlabeling • Probabilistic people trajectoryclassification • Conclusions

  14. Multi-camera consistentlabeling • Consistent labeling allows to assign the same label to different instances of the same person in different cameras • Consistent labeling can be exploited not only for people tracking on wide scenes, but also for posterity logging; multiple views of the same person exploited to improve retrieval for post-analysis • HECOL (Homography and Epipolar-based Consistent Labeling): pure geometrical approach [3] [3] S. CALDERARA, R. CUCCHIARA, PRATI A. Bayesian-competitiveConsistentLabelingfor People Surveillance. In press on IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

  15. The HECOL system

  16. correct correspondence The HECOL system (2) • Off-line process automatically computes ground-plane homography and epipolar constraints • On-line process employs Bayesian-competitive approach with warping of vertical axis and a two-contributions check epipolar geometry • MAP label assignment: homography a2j FG <a1j ,a2j> a1j homography

  17. Agenda • Motivations • The IMAGELAB distributed video surveillance system • Motion detection and tracking • Multi-camera consistent labeling • Probabilistic people trajectory classification • Motivations • The IMAGELAB distributed video surveillance system • Motion detection and tracking • Multi-camera consistentlabeling • Probabilistic people trajectoryclassification • Conclusions

  18. Trajectoryacquisition

  19. Pathmodeling • Each trajectory is encoded as a sequence of directions: • Each trajectory is modeled as a Von Mises distribution where I0 is modified zero-order Bessel function of the first kind. • The Von Mises parameters are learnt through ML estimator:

  20. Learningphase • A training set of trajectories has been collected: 88 trajectories to train the classifier and model the concept of normality • Aiming at clustering similar trajectories, not similar directions • Parameters’ space clustered with k-medoids, because it is not Euclidean; specific distance metric based on Bhattacharyya distance • Analytical and closed-form expression derived for two Von Mises distributions:

  21. Clusteringtrajectories

  22. Clusteringtrajectories (2) • The medoids constitute the model of normal behavior and are used to build a multimodal mixture distribution of “normality”: • The final distribution is a mixture of Von Mises distribution • The components are the Von Misespdf of each medoid. • The weights of each component is proportional to the number of training trajectories that fall into a specific cluster • The influence of abnormal trajectories acquired during training is smoothed by clustering and mixture component weighting coefficients

  23. Testingphase • Classification of a new path Tj is performed by a two-steps approach that: • first selects the best candidate model among the available medoids • and subsequently tests its fitness with the observed data First step Secondstep

  24. Testingphase (2) • Model selection exploiting MAP framework to maximize the model’s parameters posterior over the observed path directions (a discrete hidden 1-of-K variable Z is introduced) • After the evaluation, the desired model parameters are selected according to the Zi value that maximizes the posterior:

  25. Testingphase (3) • After the selection of the model, the path is verified against the model using a first-order Bayesian network: • Right side decoupled in two contributions, the first coming by the fitness of the variable against the selected model and the second coming from the range of variability with respect to the previous observed value:

  26. Resultsforpathclassification • Testing: more than two hours of logging, two sets (of 121 and 135 trajectories) • Ground truths based on judges of experts. The experts divided the trajectories into 95 abnormal and 161 normal. • The classification rate is 100% for abnormal and 97.5% for normal. • It is important to observe that the system correctly detects all the abnormal trajectories generating only false warnings in the case of normal behavior erroneously classified.

  27. Conclusions • Complete system for distributed video surveillance presented • Single steps commented and results shown • Details on high-level trajectory classification for abnormal path detection • The overall system has been deeply tested on campus-based controlled scenario • Preliminary setup on park (uncontrolled) scenario shows promising results

  28. Thankyou!!

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