CAMTrack: a combined appearance-motion method for multiple-object tracking

Object tracking has emerged as an essential process for various applications in the field of computer vision, such as autonomous driving. Recently, object tracking technology has experienced rapid growth, particularly its applications in self-driving vehicles. Tracking systems typically follow the detection-based tracking paradigm, which is affected by the detection results. Although deep learning has led to significant improvements in object detection, data association remains dependent on factors such as spatial location, motion, and appearance, to associate new observations with existing tracks. In this study, we introduce a novel approach called Combined Appearance-Motion Tracking (CAMTrack) to enhance data association by integrating object appearances and their corresponding movements. The proposed tracking method utilizes an appearance-motion model using an appearance-affinity network and an Interactive Multiple Model (IMM). We deploy the appearance model to address the visual affinity between objects across frames and employed the motion model to incorporate motion constraints to obtain robust position predictions under maneuvering movements. Moreover, we also propose a Two-phase association algorithm which is an effective way to recover lost tracks back from previous frames. CAMTrack was evaluated on the widely recognized object tracking benchmarks-KITTI and MOT17. The results showed the superior performance of the proposed method, highlighting its potential to contribute to advances in object tracking.