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A System for Learning Statistical Motion Patterns
September 2006 (vol. 28 no. 9)
pp. 1450-1464
ASCII Text
x 
 
Weiming Hu, Xuejuan Xiao, Zhouyu Fu, Dan Xie, Tieniu Tan, Steve Maybank, "A System for Learning Statistical Motion Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1450-1464, September, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2006.176,
author = {Weiming Hu and Xuejuan Xiao and Zhouyu Fu and Dan Xie and Tieniu Tan and Steve Maybank},
title = {A System for Learning Statistical Motion Patterns},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {28},
number = {9},
issn = {0162-8828},
year = {2006},
pages = {1450-1464},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.176},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A System for Learning Statistical Motion Patterns
IS - 9
SN - 0162-8828
SP1450
EP1464
EPD - 1450-1464
A1 - Weiming Hu,
A1 - Xuejuan Xiao,
A1 - Zhouyu Fu,
A1 - Dan Xie,
A1 - Tieniu Tan,
A1 - Steve Maybank,
PY - 2006
KW - Tracking multiple objects
KW - learning statistical motion patterns
KW - anomaly detection
KW - behavior understanding.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K{\hbox{-}}\rm means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

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Index Terms:
Tracking multiple objects, learning statistical motion patterns, anomaly detection, behavior understanding.
Citation:
Weiming Hu, Xuejuan Xiao, Zhouyu Fu, Dan Xie, Tieniu Tan, Steve Maybank, "A System for Learning Statistical Motion Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1450-1464, Sept. 2006, doi:10.1109/TPAMI.2006.176
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