Multi-target tracking with hierarchical data association using main-parts and spatial-temporal feature models

Abstract

Multi-target tracking in complex scenes is challenging because target appearance features generate partial or significant variations frequently. In order to solve the problem, we propose a multi-target tracking method with hierarchical data association using main-parts and spatial-temporal feature models. In our tracking framework, target feature models and tracklets are initialized when the new targets appear. Main-parts feature model is presented to represent target with partial or no appearance variations. It is established by partitioning a target template into several parts and formulating appearance variation densities of these parts. For the target with significant appearance variations, the tracker learns its global spatial-temporal feature model by integrating appearance with histogram of optical flow features. During tracking, tracklet confidence is exploited to implement hierarchical data association. According to different tracklet confidence values, main-parts and global data association are respectively performed by employing main-parts and spatial-temporal feature models. As a result, our approach uses the Hungarian algorithm to obtain optimal associated pairs between target tracklets and detections. Finally, target feature models and tracklets are updated by the association detections for subsequently tracking. Experiments conducted on CAVIAR, Parking Lot and MOT15 datasets verify the effectiveness and improvement of our multi-target tracking method.