Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection

TL;DR

This paper introduces a novel unsupervised method for pedestrian video anomaly detection that uses trajectory prediction errors to identify abnormal behaviors, improving detection accuracy and efficiency.

Contribution

It proposes a trajectory prediction-based framework for anomaly detection, differing from previous reconstruction methods, and demonstrates its effectiveness on real-world datasets.

Findings

Effective detection of pedestrian anomalies in videos

Outperforms previous reconstruction-based methods

Validated on multiple real-world benchmark datasets

Abstract

Video anomaly detection is a core problem in vision. Correctly detecting and identifying anomalous behaviors in pedestrians from video data will enable safety-critical applications such as surveillance, activity monitoring, and human-robot interaction. In this paper, we propose to leverage trajectory localization and prediction for unsupervised pedestrian anomaly event detection. Different than previous reconstruction-based approaches, our proposed framework rely on the prediction errors of normal and abnormal pedestrian trajectories to detect anomalies spatially and temporally. We present experimental results on real-world benchmark datasets on varying timescales and show that our proposed trajectory-predictor-based anomaly detection pipeline is effective and efficient at identifying anomalous activities of pedestrians in videos. Code will be made available at https://github.com/akanuasiegbu/Leveraging-Trajectory-Prediction-for-Pedestrian-Video-Anomaly-Detection.