Masked Convolutions within Skip Connections for Video Anomaly Detection

Abstract

Video anomaly detection plays a crucial role in various fields such as surveillance, health monitoring, and industrial quality control. This research paper introduces a novel contribution to the field by presenting MaskedConv3D layers within a modified UNet architecture. These MaskedConv3D layers provide a unique approach to information propagation in three-dimensional video data by selectively masking temporal regions of convolutional kernels. By incorporating these layers into the skip connections of the UNet, the model gains the ability to infer missing information in the temporal domain based on the surrounding context. This innovative mechanism enhances the preservation of spatial and temporal details, addressing the challenge of effectively detecting anomalies in video data. The proposed methodology is evaluated on popular video datasets, showcasing its effectiveness in capturing intricate patterns and contexts. The results highlight the superiority of the modified UNet with MaskedConv3D layers compared to traditional approaches. Overall, this research introduces a novel technique for information propagation in video data and demonstrates its potential for advancing video anomaly detection.

Keywords: Video Anomaly Detection, Masked Convolutions, UNets