Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection

• URL: http://arxiv.org/abs/2207.02279v1
• Date: Tue, 5 Jul 2022 19:44:34 GMT
• Title: Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection
• Authors: Asiegbu Miracle Kanu-Asiegbu, Ram Vasudevan, Xiaoxiao Du
• Abstract summary: We propose to leverage trajectory localization and prediction for unsupervised pedestrian anomaly event detection. 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.
• Score: 14.740178121212132
• License: http://creativecommons.org/licenses/by/4.0/
• 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-Detect ion.

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