Name
Title | ||
|---|---|---|
Anomaly detection based on spatio-temporal sparse representation and visual attention analysis. |
Abstract | ||
|---|---|---|
In this paper, we proposed a unified framework for anomaly detection and localization in crowed scenes. For each video frame, we extract the spatio-temporal sparse features of 3D blocks and generate the saliency map using a block-based center-surround difference operator. Two sparse coding strategies including off-line long-term sparse representation and on-line short-term sparse representation are integrated within our framework. Abnormality of each candidate is measured using bottom-up saliency and top-down fixation inference and further used to classify the frames into normal and anomalous ones by a binary classifier. Local abnormal events are localized and segmented based on the saliency map. In the experiments, we compared our method against several state-of-the-art approaches on UCSD data set which is a widely used anomaly detection and localization benchmark. Our method outputs competitive results with near real-time processing speed compared to state-of-the-arts. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-015-3199-8 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Sparse representation,Anomaly detection,Visual learning,Visual attention model,Fixation inference,Anomaly localization,ROC,Independent component analysis,Maximum a posterior | Computer vision,Anomaly detection,Binary classification,Pattern recognition,Computer science,Inference,Neural coding,Salience (neuroscience),Sparse approximation,Artificial intelligence,Independent component analysis,Operator (computer programming) | Journal |
Volume | Issue | ISSN |
76 | 5 | 1380-7501 |
Citations | PageRank | References |
5 | 0.38 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Wang | 1 | 108 | 5.96 |
| Hongxun Yao | 2 | 2485 | 156.65 |
| Xiaoshuai Sun | 3 | 623 | 58.76 |