论文标题
利用人工视频异常检测的轨迹预测
Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection
论文作者
论文摘要
视频异常检测是视觉中的核心问题。正确检测和识别从视频数据中的行人中的行为异常行为将使安全至关重要的应用，例如监视，活动监测和人类机器人互动。在本文中，我们建议利用无监督的行人异常事件检测的轨迹定位和预测。与以前的基于重建的方法不同，我们提出的框架依赖于正常和异常行人轨迹的预测误差来在空间和时间上检测异常。我们在不同的时间尺度上介绍了有关实际基准数据集的实验结果，并表明我们提出的基于轨迹的基于轨迹的异常检测管道在识别视频中行人的异常活动方面有效有效。代码将在https://github.com/akanuasiegbu/leveraging-trajectory-prediction-for-pedestrian-video-anomaly-detection上提供。
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.
