Name
Title | ||
|---|---|---|
Visual abnormal behavior detection based on trajectory sparse reconstruction analysis |
Abstract | ||
|---|---|---|
Abnormal behavior detection has been one of the most important research branches in intelligent video content analysis. In this paper, we propose a novel abnormal behavior detection approach by introducing trajectory sparse reconstruction analysis (SRA). Given a video scenario, we collect trajectories of normal behaviors and extract the control point features of cubic B-spline curves to construct a normal dictionary set, which is further divided into Route sets. On the dictionary set, sparse reconstruction coefficients and residuals of a test trajectory to the Route sets can be calculated with SRA. The minimal residual is used to classify the test behavior into a normal behavior or an abnormal one. SRA is solved by L1-norm minimization, leading to that a few of dictionary samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is valid even when the dictionary set is very small. Experimental results with comparisons show that the proposed approach improves the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neucom.2012.03.040 | Neurocomputing |
Keywords | Field | DocType |
normal behavior,novel abnormal behavior detection,test behavior,route set,dictionary set,trajectory sparse reconstruction analysis,dictionary sample,normal dictionary set,abnormal behavior detection,behavior trajectory,visual abnormal behavior detection | Residual,Control point,K-SVD,Pattern recognition,Abnormality,Video content analysis,Minification,Artificial intelligence,Trajectory,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
119, | 0925-2312 | 25 |
PageRank | References | Authors |
0.70 | 30 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ce Li | 1 | 56 | 9.28 |
| Zhenjun Han | 2 | 176 | 16.40 |
| Qixiang Ye | 3 | 913 | 64.51 |
| Jianbin Jiao | 4 | 367 | 32.61 |