Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi View Image Surveillance and Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Automatic Learning of an Activity-Based Semantic Scene Model
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Scene Segmentation for Behaviour Correlation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trajectory Association and Fusion across Partially Overlapping Cameras
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Correspondence-Free Activity Analysis and Scene Modeling in Multiple Camera Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding
International Journal of Computer Vision
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Extracting Pathlets FromWeak Tracking Data
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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We present a novel multi-camera framework to extract reliable pathlets [1] from tracking data. The proposed approach weights tracks based on their spatial and orientation similarity to simultaneous tracks observed in other camera views. The weighted tracks are used to build a Markovian state space of the environment and Spectral Clustering is employed to extract pathlets from a state-wise similarity matrix. We present experimental results on five multi-camera datasets collected under varying weather conditions and compare with pathlets extracted from individual camera views and three other multi-camera algorithms.