Enhanced deep learning model for anomaly object detection and tracking from surveillance videos.

Abstract

Video anomaly detection plays a crucial role in video surveillance, which identifies suspicious intruders without human intervention. Moreover, the rapid growth of video surveillance applications such as intrusion detection, health monitoring systems, and fault detection provides a secure environment. Furthermore, detecting anomalous intruders from video is a challenging task because of diverse contexts, lack of training data, and environmental variations. Several conventional techniques use various Deep Learning algorithms for anomaly detection, which possess limitations including high false positive rates and occlusion. Therefore, to overcome the drawbacks, efficient anomaly object detection and tracking system is proposed using an enhanced wolf Crocuta optimization-based deep Bidirectional Long Short-Term Memory (EnWC-DBiLSTM) classifier. Here, an effective keyframe selection is attained by the Timber Prairie Wolf Optimization (TPWO) strategy, which optimally selects the required keyframes for further processing. Further, the combination DBiLSTM classifier processes the input data accurately and detects the target object, in both directions. Moreover, the enhanced Wolf Crocuta optimization (EnWC) helps to eliminate local power resolution, which improves the convergence speed of the model. Henceforth, the proposed model achieved an accuracy of 98.226%, equal error rate, sensitivity, and specificity of 1.774, 98.111%, and 99.551%, respectively, for the ShanghaiTech campus dataset.