Long Short Working Memory (LSWM) Integration with Polynomial Connectivity for Object Tracking in Wide Area Motion Imagery

摘要

High-value-target tracking in full-motion-video is a difficult surveillance task due to model drift while online training. We propose a tracker that adaptively fuses detections from multiple target models trained using three memory types to overcome model drift and other challenges. The short-term memory uses correlation and histogram features to detect the target from its recent appearance. The working memory uses a deep extreme learning network with polynomial connectivity that is trained online using a set of target appearances and background from the recent past. The long-term memory uses an offline trained polynomial CNN as a vehicle detector. Occlusion detection along with image registration and motion estimation stages aid in tracking the target through occlusions. A motion detection module reduces the effects of model drift and the scale change detector keeps the boundary accurate to the target. The proposed tracker is evaluated against state-of-the-art tracking methods on a vehicle dataset that includes challenging scenarios such as tree canopy occlusion, shadow, and erratic vehicle motion.