Name
Title | ||
|---|---|---|
Improving Video Anomaly Detection Performance By Mining Useful Data From Unseen Video Frames |
Abstract | ||
|---|---|---|
Existing state-of-the-art (SOTA) video anomaly detection methods have mainly focused on the network design for obtaining their performance improvements. Different to the main research trend, this paper focuses on the data perspective, where the key idea is to mine useful 'normal' data from the unseen video frames. For any off-the-shelf anomaly deep model, these newly mined data could help the deep model in familiarizing more normal feature patterns. Thus, those previously miss-detected abnormal patterns and false-alarms detections would have more chances to be rectified if the target anomaly detection deep model has been finetuned on these newly mined normal data. Extensive quantitative evaluations have verified the effectiveness of the proposed approach in achieving persistent performance improvement by almost 7% AUC improvement. (c) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.05.112 | NEUROCOMPUTING |
Keywords | DocType | Volume |
Anomaly detection, Semi-supervised learning | Journal | 462 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wu Renzhi | 1 | 2 | 1.72 |
| Shuai Li | 2 | 0 | 0.34 |
| Chenglizhao Chen | 3 | 113 | 14.22 |
| Aimin Hao | 4 | 0 | 0.34 |