Existing CNN-based video anomaly detection methods improve the accuracy continuously, which are faced with issues such as complex architecture, large parameters and lengthy training. Therefore, the hardware computing power requirements of them are high, which makes it difficult to adapt to edge devices with limited computing resources like UAVs. To this end, this paper proposed a lightweight abnormal event detection method for edge devices. Firstly, the method extracted gradient cuboids and optical flow cuboids from video sequence as appearance and motion feature representation. Secondly, the method designed a modified PCANet network to obtain high-level block-wise histogram features of gradient cuboids. Then, the method calculated the appearance anomaly score of each block based on histogram feature distribution, and calculated the motion anomaly score based on the accumulation of optical flow amplitudes of internal pixels. Finally, the method fused the appearance and motion anomaly scores to identify anomalous blocks, achieving appearance and motion abnormal events detection and localization simultaneously. The frame-level AUC of proposed method reached 86.7% on UCSD Ped1 dataset and 94.9% on UCSD Ped2 dataset, which were superior to other methods and the parameters were much smaller. Experimental results show that the method achieves better anomaly detection performance under low computational power requirements, making the balance between detection precision and computing resources, which is suitable for low-power edge devices.

[2023-08-14] Accepted Paper /doc/2023.04.0225/v1

[2024-01-05] Printed Article /doc/2023.04.0225/v2