YOLO v7 - Distance Intersection of Union for Detecting Objects and Anomalies in Video Surveillance

Surveillance videos are essential for crime prevention and public safety.However, defining abnormal events remains challenging, which hinders their effectiveness and limits the use of supervised techniques.The existing method have difficulty in accurately detecting and tracking the objects, that minimizes the detection and tracking performance.In this research, the You Only Look Once v7 (YOLO v7) -Distance Intersection of Union (D-IoU) and Earth Mover Distance (EMD) approach is proposed to detect and track the objects and anomalies in video surveillance.The D-IoU loss function is used in the YOLO v7 model improves the precision of bounding box prediction by considering the distance between centres of the bounding box, which is useful for the accurate localization of object.Then, the features are extracted by using the Inception V3 approach that extracts the meaningful features that help differentiate the anomalies in the detected object.To detect the anomalies, the EMD method is used which effectively detects the anomalies.The YOLO v7 -D-IoU and EMD approach obtained 96.1% accuracy on UCSD Ped 1 datasets and 98.8% accuracy on UCSD Ped 2 dataset.The proposed method showed effective performance when compared to conventional methods like Three-Dimensional Convolutional Neural Network (3D-CNN).