Abstract
Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality detection. We present an in-depth study of two types of sparse codes solutions-greedy algorithms and convex L1-norm solutions-and their impact on abnormality detection performance. We also propose our framework of combining sparse codes with different detection methods. Our comparative experiments are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. The experiment results show that combining OMP codes with maximum coordinate detection could achieve state-of-The-Art performance on the UCSD dataset [14].
| Original language | English |
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| Title of host publication | 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
| Number of pages | 7 |
| Publisher | IEEE |
| Publication date | 2016 |
| Pages | 66-72 |
| Article number | 7738016 |
| ISBN (Electronic) | 978-1-5090-3811-4 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 13th IEEE International Conference on Advanced Video and Signal Based Surveillance - University of Colorado, Colorado Springs, United States Duration: 23 Aug 2016 → 26 Aug 2016 Conference number: 13 |
Conference
| Conference | 13th IEEE International Conference on Advanced Video and Signal Based Surveillance |
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| Number | 13 |
| Location | University of Colorado |
| Country/Territory | United States |
| City | Colorado Springs |
| Period | 23/08/2016 → 26/08/2016 |