Enhancing anomaly detection performance using ResNet50 and BiLSTM networks on benchmark datasets

Abstract

Detection of abnormal activity from large video sequences is one of the biggest challenges because of ambiguity in different activities. Over the last many years, several cameras have been placed to cover the public and private sectors to monitor abnormal human activity and surveillance. In recent years, deep learning and computer vision have significantly impacted this kind of surveillance. Intelligent systems that can automatically identify unusual events in video streams are currently in high demand. A deep learning-based combinational model has been proposed to detect abnormal activity from input video streams. The proposed study uses a combination of convolution and sequential models. A ResNet50 network with a residual connection was used for initial feature extraction. The proposed bidirectional long short-term memory (BiLSTM) network has improved the extracted ResNet50 features. Simulation of the proposed model was experimented on two benchmark datasets for anomaly detection UCF Crime and ShanghaiTech. Simulation of proposed architecture has achieved 97.55% and 91.94% remarkable accuracy for UCF Crime and ShanghaiTech datasets respectively.