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 here.
Links
Citation
Plain Text:
A. M. Kanu-Asiegbu, R. Vasudevan, X. Du, “Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection,” in IEEE Symposium Series on Computational Intelligence (SSCI), pp. 01-08, 2021.
BibTex:
@INPROCEEDINGS{kanu2021leveraging,
author={Kanu-Asiegbu, Asiegbu Miracle and Vasudevan, Ram and Du, Xiaoxiao},
booktitle={IEEE Symposium Series on Computational Intelligence (SSCI)},
title={Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection},
pages={01-08},
year={2021}}