Object-Aware Skeleton-Based Anomaly Detection in Surveillance Videos

Abstract

This paper proposes an object-aware skeleton-based anomaly detection method for surveillance videos. The previous skeleton-based anomaly detection approaches learn to reconstruct normal skeleton patterns solely from the skeleton information. However, such methods suffer from detecting object-related abnormal behavior, which has a similar skeleton pose to normal behavior (e.g., riding bicycles/motorcycles). To improve the detection accuracy of such anomalies, we propose incorporating the information of objects (bounding boxes and class labels) around humans. The object and skeleton information are jointly processed through an encoder-decoder RNN to reconstruct the information. We evaluate the proposed method on the HR-ShanghaiTech dataset and achieve an accuracy improvement of 3.1%, reaching 78.2% in the best model.