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Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM
| Content Provider | MDPI |
|---|---|
| Author | Ma, Zhen Machado, José J. M. Tavares, João Manuel R. S. |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 7508 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21227508 |
| Journal | Sensors |
| Issue Number | 22 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-12 |
| Access Restriction | Open |
| Subject Keyword | Sensors Video Anomaly Detection Three-dimensional Convolution Lstm Weakly Supervised Spatial-temporal Features Max-pooling |
| Content Type | Text |
| Resource Type | Article |
