Long-term Object Tracking Based on Improved Continuously Adaptive Mean Shift Algorithm

Abstract

Long-term object tracking encounters complex scene changes, such as deformation, short-term departure from sight, occlusion, and lighting changes, resulting in complex and unstable tracking. To improve the accuracy and success rate of long-term object tracking in complex scenes, an improved continuously adaptive mean shift (CAMShift) algorithm was proposed. The joint probability density distribution of the target model was obtained by using the Bhattacharrya coefficient to calculate the contribution of the color features and texture features. Combining with the fused target model and Kalman filter, the target position was obtained by implementing CAMShift algorithm. Finally, a template pool was designed to store high-confidence tracking results. The target template was updated online by retrieving the initial frame from the template pool to recover re-detection after tracking drift or failure. The accuracy of the proposed algorithm was verified by simulation analysis. Results show that the distance precision and success rate of the proposed algorithm are 0.9 and 0.83, respectively. The proposed algorithm effectively solves long-term target tracking problems affected by complex scenes, such as occlusion, similar colors, and deformation. This study provides references for the automatic detection of traffic incidents in the intelligent traffic monitoring system.