Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos. Especially, the prediction based approach is one of the most studied methods to detect anomalies by predicting frames that include abnormal events in the test set after learning with the normal frames of the training set. However, a lot of prediction networks are computationally expensive owing to the use of pre-trained optical flow networks, or fail to detect abnormal situations because of their strong generative ability to predict even the anomalies. To address these shortcomings, we propose spatial rotation transformation (SRT) and temporal mixing transformation (TMT) to generate irregular patch cuboids within normal frame cuboids in order to enhance the learning of normal features. Additionally, the proposed patch transformation is used only during the training phase, allowing our model to detect abnormal frames at fast speed during inference. Our model is evaluated on three anomaly detection benchmarks, achieving competitive accuracy and surpassing all the previous works in terms of speed.
|Title of host publication||Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||11|
|Publication status||Published - 2022|
|Event||22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 - Waikoloa, United States|
Duration: 2022 Jan 4 → 2022 Jan 8
|Name||Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022|
|Conference||22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022|
|Period||22/1/4 → 22/1/8|
Bibliographical notePublisher Copyright:
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Computer Science Applications