OBJECT TRACKING AND ANOMALY DETECTION IN FULL MOTION VIDEO

High volume of Full Motion Videos (FMVs) require development of automated tools to help reduce the cognitive burden of the analysts. The number of algorithms for object detection, classification, tracking and anomaly detection in FMV were investigated. The object detection and classification was performed using YOLOv4 technique. Two approaches for object tracking were analyzed: (i) short-term tracking approach based on DeepSORT and (ii) persistent tracking based on template matching and structural similarity index. Anomaly detection and pattern of life analysis algorithms based on trajectory clustering and time series analysis were tested on simulated data and real FMV over a highway with traffic.