Large-scale statistical modeling of motion patterns: a Bayesian nonparametric approach

Authors:
Santu Rana;Dinh Phung;Sonny Pham;Svetha Venkatesh
Affiliations:
Deakin University, Waurn Ponds, VIC;Deakin University, Waurn Ponds, VIC;Curtin University, Bentley, WA;Deakin University, Waurn Ponds, VIC
Venue:
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Year:
2012

Citing 12
Cited 0

Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance

International Journal of Computer Vision
On Space-Time Interest Points

International Journal of Computer Vision
Comparing clusterings: an axiomatic view

ICML '05 Proceedings of the 22nd international conference on Machine learning
A hierarchical Bayesian language model based on Pitman-Yor processes

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient duration and hierarchical modeling for human activity recognition

Artificial Intelligence
Effective Anomaly Detection in Sensor Networks Data Streams

ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models

International Journal of Computer Vision
Decayed MCMC iltering

UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning semantic scene models by trajectory analysis

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but different activity levels (eg. car vs bike). We propose a modification of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection.