Name
Abstract | ||
|---|---|---|
Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21227508 | SENSORS |
Keywords | DocType | Volume |
video anomaly detection, three-dimensional convolution, LSTM, weakly supervised, spatial-temporal features, max-pooling | Journal | 21 |
Issue | ISSN | Citations |
22 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Ma | 1 | 0 | 0.34 |
| José J M Machado | 2 | 0 | 0.34 |
| João Manuel R S Tavares | 3 | 0 | 0.34 |