Bilayer Sparse Topic Model for Scene Analysis in Imbalanced Surveillance Videos
- 14 October 2014
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 23 (12), 5198-5208
- https://doi.org/10.1109/tip.2014.2363408
Abstract
Dynamic scene analysis has become a popular research area especially in video surveillance. The goal of this paper is to mine semantic motion patterns and detect abnormalities deviating from normal ones occurring in complex dynamic scenarios. To address this problem, we propose a data-driven and scene-independent approach, namely, Bilayer sparse topic model (BiSTM), where a given surveillance video is represented by a word-document hierarchical generative process. In this BiSTM, motion patterns are treated as latent topics sparsely distributed over low-level motion vectors, whereas a video clip can be sparsely reconstructed by a mixture of topics (motion pattern). In addition to capture the characteristic of extreme imbalance between numerous typical normal activities and few rare abnormalities in surveillance video data, a one-class constraint is directly imposed on the distribution of documents as a discriminant priori. By jointly learning topics and one-class document representation within a discriminative framework, the topic (pattern) space is more specific and explicit. An effective alternative iteration algorithm is presented for the model learning. Experimental results and comparisons on various public data sets demonstrate the promise of the proposed approach.Keywords
Funding Information
- 863 Program (2014AA015104)
- National Natural Science Foundation of China (61273034, 61332016)
This publication has 20 references indexed in Scilit:
- Learning Semantic Motion Patterns for Dynamic Scenes by Improved Sparse Topical CodingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Human action recognition by learning bases of action attributes and partsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Sparse reconstruction cost for abnormal event detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Online detection of unusual events in videos via dynamic sparse codingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic ModelIEEE Transactions on Pattern Analysis and Machine Intelligence, 2011
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- A Duality Based Approach for Realtime TV-L 1 Optical FlowPublished by Springer Science and Business Media LLC ,2007
- Unsupervised Activity Perception by Hierarchical Bayesian ModelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Estimating the Support of a High-Dimensional DistributionNeural Computation, 2001
- Learning patterns of activity using real-time trackingIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000