Optimal deep transfer learning enabled object detector for anomaly recognition in pedestrian ways

Anomaly detection is a branch of behavior understanding in surveillance scenes, where anomalies represent a deviation in the behavior of scene entities (viz.,humans, vehicles, and environment) from regular patterns. In pedestrian walkways, this plays a vital role in enhancing safety. With the widespread use of video surveillance systems and the escalating video volume, manual examination of abnormal events becomes time-intensive.Hence, the need for an automated surveillance system adept at anomaly detection is crucial, especially within the realm of computer vision (CV) research. The surge in interest towards deep learning (DL) algorithms has significantly impacted CV techniques, including object detection and classification. Unlike traditional reliance on supervised learning requiring labeled datasets, DL offers advancements in these applications. Thus, this study presents an Optimal Deep Transfer Learning Enabled Object Detector for Anomaly Recognition in Pedestrian Ways (ODTLOD-ARPW) technique. The purpose of the ODTLOD-ARPW method is to recognize the occurrence of anomalies in pedestrian walkways using a DL-based object detector. In the ODTLOD-ARPW technique, the image pre-processing initially takes place using two sub-processes namely Wiener filtering (WF) based pre-processing and dynamic histogram equalization-based contrast enhancement. For anomaly detection, the ODTLOD-ARPW technique employs the YOLOV8s model which offers enhanced accuracy and performance. The hyperparameter tuning process takes place using a root mean square propagation (RMSProp) optimizer. The performance analysis of the ODTLOD-ARPW method is tested under the UCSD anomaly detection dataset. An extensive comparative study reported that the ODTLOD-ARPW technique reaches an effective performance with other models with maximum accuracy of 98.67%.