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arXiv · 2207.02279

Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection

Asiegbu Miracle Kanu-Asiegbu·Ram Vasudevan·Xiaoxiao Du
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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.

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Asiegbu Miracle Kanu-Asiegbu, Ram Vasudevan, Xiaoxiao Du. 2022-07-05. Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection. https://doi.org/10.1109/ssci50451.2021.9660004

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