Video Anomaly Detection in Overlapping Data: The More Cameras, the Better?

Video anomaly detection (VAD) has been densely explored in the last few years, mostly in single-camera scenarios. Despite significant advancements in this field, effectiveness is still seriously compromised in challenging environments (e.g., varying lighting conditions, under partial occlusions, and in crowded environments). For the sake of affordable data annotation, the most relevant methods assume the weakly supervised paradigm, where the label is available only at the video level (WS-VAD). Also, these methods are conventionally designed for single-camera mode and do not consider the multi-view information yielded from overlapping surveillance cameras, which is very common in practical scenarios. In this work, we started by systematically evaluating the WS-VAD performance that can be attained when different camera combinations are used as data sources. Interestingly, we observed that the rule "the more cameras, the better" should not be assumed, as there were always particular subsets of cameras that consistently outperformed the remaining configurations. Upon these conclusions, we present a semi-automated procedure to identify the optimal camera sources based on the image features/characteristics (distance, pose, and lighting) each one is capturing. Extensive experiments were carried out in three overlapping multi-camera datasets, which suggest that 1) multi-camera schemes consistently outperform single-camera methods and - most interestingly - 2) the correlation between the data acquired by the different cameras severely impacts performance, turning the selection of cameras a crucial step in VAD. Our findings open an intriguing research topic about methods/algorithms that filter out/select the camera sources we should use in overlapping camera scenarios.