CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using computer vision to simulate the motion of virtual agents: Research Articles
Computer Animation and Virtual Worlds
Semantic classification of human behaviors in video surveillance systems
WSEAS Transactions on Computers
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Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Event Detection Using Trajectory Clustering and 4-D Histograms
IEEE Transactions on Circuits and Systems for Video Technology
A Network of Dynamic Probabilistic Models for Human Interaction Analysis
IEEE Transactions on Circuits and Systems for Video Technology
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Context: As trajectory analysis is widely used in the fields of video surveillance, crowd monitoring, behavioral prediction, and anomaly detection, finding motion patterns is a fundamental task for pedestrian trajectory analysis. Objective: In this paper, we focus on learning dominant motion patterns in unstructured scene. Methods: As the invisible implicit indicator to scene structure, latent structural information is first defined and learned by clustering source/sink points using CURE algorithm. Considering the basic assumption that most pedestrians would find the similar paths to pass through an unstructured scene if their entry and exit areas are fixed, trajectories are then grouped based on the latent structural information. Finally, the motion patterns are learned for each group, which are characterized by a series of statistical temporal and spatial properties including length, duration and envelopes in polar coordinate space. Results: Experimental results demonstrate the feasibility and effectiveness of our method, and the learned motion patterns can efficiently describe the statistical spatiotemporal models of the typical pedestrian behaviors in a real scene. Based on the learned motion patterns, abnormal or suspicious trajectories are detected. Conclusion: The performance of our approach shows high spatial accuracy and low computational cost.