IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Motion Patterns in Surveillance Video using HMM Clustering
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Content-based retrieval of functional objects in video using scene context
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Functional scene element recognition for video scene analysis
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Learning semantic scene models from observing activity in visual surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Content-based retrieval of functional objects in video using scene context
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
People watching: human actions as a cue for single view geometry
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Scene semantics from long-term observation of people
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Existing methods for video scene analysis are primarily concerned with learning motion patterns or models for anomaly detection. We present a novel form of video scene analysis where scene element categories such as roads, parking areas, sidewalks and entrances, can be segmented and categorized based on the behaviors of moving objects in and around them. We view the problem from the perspective of categorical object recognition, and present an approach for unsupervised learning of functional scene element categories. Our approach identifies functional regions with similar behaviors in the same scene and/or across scenes, by clustering histograms based on a trajectory-level, behavioral codebook. Experiments are conducted on two outdoor webcam video scenes with low frame rates and poor quality. Unsupervised classification results are presented for each scene independently, and also jointly where models learned on one scene are applied to the other.