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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 are 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. [ABSTRACT FROM AUTHOR]

Most research studies on forecasting of earthquakes focus on thermal or electrical aspects. Scholars have proved GPS time series also contains precursory information for large earthquakes. For the problems mentioned before, three typical earthquakes hit America from 2001 to 2010 were investigated by using GPS data of several GPS reference stations near the epicenters. This paper proposed an algorithm based on Martingale theory to detect the anomalies in GPS data. The experimental results show that the proposed algorithm can effectively reflect the change process in GPS data before and after large earthquakes, which offers more possibilities for earthquake forecast from GPS data. [ABSTRACT FROM AUTHOR]