A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels
This work addresses the problem of reducing annotation costs for anomaly detection in surveillance videos, offering a practical solution for the video processing community, though it is incremental as it builds on existing weakly supervised methods.
The paper tackles weakly supervised anomaly detection in surveillance videos using only video-level labels, which are noisy, and proposes a self-reasoning framework with deep neural networks and binary clustering to mitigate label noise, achieving state-of-the-art results on datasets like UCF-crime, ShanghaiTech, and UCSD Ped2.
Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community. Compared to the frame-level annotations of anomalous events, obtaining video-level annotations is quite fast and cheap though such high-level labels may contain significant noise. More specifically, an anomalous labeled video may actually contain anomaly only in a short duration while the rest of the video frames may be normal. In the current work, we propose a weakly supervised anomaly detection framework based on deep neural networks which is trained in a self-reasoning fashion using only video-level labels. To carry out the self-reasoning based training, we generate pseudo labels by using binary clustering of spatio-temporal video features which helps in mitigating the noise present in the labels of anomalous videos. Our proposed formulation encourages both the main network and the clustering to complement each other in achieving the goal of more accurate anomaly detection. The proposed framework has been evaluated on publicly available real-world anomaly detection datasets including UCF-crime, ShanghaiTech and UCSD Ped2. The experiments demonstrate superiority of our proposed framework over the current state-of-the-art methods.