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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tong Lu Liang Wu Xiaolin Ma Shivakumara, P. Chew Lim Tan |
| Copyright Year | 2014 |
| Description | Author affiliation: Nat. Key Lab. of Novel Software Technol., Nanjing Univ., Nanjing, China (Tong Lu; Liang Wu; Xiaolin Ma) || Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore (Chew Lim Tan) || Fac. of Comput. Sci. & Inf. Technol., Univ. of Malaya, Kuala Lumpur, Malaysia (Shivakumara, P.) |
| Abstract | A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. During the first stage, we averagely divide the spatiotemporal volume into atomic blocks. Considering the fact that mutual interference of several human body parts potentially happen in the same block, we propose an atomic motion pattern representation using the Gaussian Mixture Model (GMM) to distinguish the motions inside each block in a refined way. Usual motion patterns can thus be defined as a certain type of steady motion activities appearing at specific scene positions. During the second stage, we further use the Markov Random Field (MRF) model to characterize the joint label distributions over all the adjacent local motion patterns inside the same crowded scene, aiming at modeling the severely occluded situations in a crowded scene accurately. By combining the determinations from the two stages, a weighted scheme is proposed to automatically detect anomaly events from crowded scenes. The experimental results on several different outdoor and indoor crowded scenes illustrate the effectiveness of the proposed algorithm. |
| Starting Page | 2203 |
| Ending Page | 2208 |
| File Size | 310550 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.383 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Manganese Context modeling Vectors Training Tracking Prototypes Context |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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