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.
翻译:视频异常现象探测是一个核心的视觉问题。 从视频数据中正确检测和识别行人异常行为将有利于安全关键应用,如监视、活动监测和人-机器人互动。 在本文中,我们提议利用轨迹定位和预测来探测无人监督的行人异常事件。与以前以重建为基础的方法不同,我们提议的框架依靠正常和异常行人轨迹的预测错误来在空间和时间上探测异常现象。我们介绍了不同时间尺度上真实世界基准数据集的实验结果,并表明我们提议的轨迹定位异常探测管道能够有效和高效地识别行人视频中的异常活动。代码将在https://github.com/kanuasiegbu/Leverageg-Trajotory-prospect-for-Pedestrian-Video-Anomaly-探测器上公布。