A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views
This work addresses anomaly detection for surveillance systems by mitigating occlusion and clutter issues, though it is incremental as it builds on existing MIL and multi-view techniques.
The paper tackled anomaly detection in surveillance videos by combining Multiple Instance Learning (MIL) to handle label scarcity and Multiple Camera Views (MC) to reduce occlusion and clutter, resulting in a significant performance improvement in F1 score compared to single-camera methods.
Occlusion and clutter are two scene states that make it difficult to detect anomalies in surveillance video. Furthermore, anomaly events are rare and, as a consequence, class imbalance and lack of labeled anomaly data are also key features of this task. Therefore, weakly supervised methods are heavily researched for this application. In this paper, we tackle these typical problems of anomaly detection in surveillance video by combining Multiple Instance Learning (MIL) to deal with the lack of labels and Multiple Camera Views (MC) to reduce occlusion and clutter effects. In the resulting MC-MIL algorithm we apply a multiple camera combined loss function to train a regression network with Sultani's MIL ranking function. To evaluate the MC-MIL algorithm first proposed here, the multiple camera PETS-2009 benchmark dataset was re-labeled for the anomaly detection task from multiple camera views. The result shows a significant performance improvement in F1 score compared to the single-camera configuration.