Human activity-based anomaly detection and recognition by surveillance video using kernel local component analysis with classification by deep learning techniques

Abnormal behavior methods have attempted to reduce execution time, computational complexity, efficiency, robustness against pixel occlusion, and generalizability. This research proposed a novel method in human activity-based anomaly detection and recognition by surveillance video utilizing DL methods. Input is collected as video and processed for noise removal and smoothening. Then kernel local component analysis extracts these video features for human activity monitoring. Then the extracted features are classified using Bayesian network-based spatiotemporal neural networks. The classified output shows the anomaly activities of the selected input surveillance video dataset. The simulation results are obtained for various crowd datasets regarding the mean average error, mean square error, training accuracy, validation accuracy, specificity, and F_measure. The proposed technique attained an MAE of 58%, MSE of 63%, specificity of 89%, and F-measure of 68%. training and validation accuracy of 92% and 96% respectively.