Object Matching in Distributed Video Surveillance Systems by LDA-Based Appearance Descriptors
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding
International Journal of Computer Vision
Activity based matching in distributed camera networks
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Vision, logic, and language - toward analyzable encompassing systems
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Abnormality detection using low-level co-occurring events
Pattern Recognition Letters
Unsupervised discovery of activity correlations using latent topic models
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Exploiting multiple cameras for environmental pathlets
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Multicamera video summarization from optimal reconstruction
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
International Journal of Computer Vision
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
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We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. In this paper, we refer to activities as motion patterns of objects, which correspond to paths in far-field scenes. We assume that the topology of cameras is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are first tracked in each camera view independently, and the positions and velocities of objects along trajectories are computed as features. Under a probabilistic model, our approach jointly learns the distribution of an activity in the feature spaces of different camera views. Then, it accomplishes the following tasks: 1) grouping trajectories, which belong to the same activity but may be in different camera views, into one cluster; 2) modeling paths commonly taken by objects across multiple camera views; and 3) detecting abnormal activities. Advantages of this approach are that it does not require first solving the challenging correspondence problem, and that learning is unsupervised. Even though correspondence is not a prerequisite, after the models of activities have been learned, they can help to solve the correspondence problem, since if two trajectories in different camera views belong to the same activity, they are likely to correspond to the same object. Our approach is evaluated on a simulated data set and two very large real data sets, which have 22,951 and 14,985 trajectories, respectively.