Affiliations: [a]
Key Laboratory of Intelligent Information Processing in Universities of Shandong (Shandong Institute of Business and Technology), Yantai, China
| [b]
Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China
| [c]
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
| [d]
School of Information Science and Engineering, Shandong Normal University, Jinan, China
Correspondence:
[*]
Corresponding author. Zhiyong an, Key Laboratory of Intelligent Information Processing in Universities of
Shandong (Shandong Institute of Business and Technology), Yantai 264005, China. anzhiyong@sdibt.edu.cn.
Abstract: Occlusion handling is a challenging problem in object tracking. Most existing methods fail to handle well in complex image sequences. This paper presents a scene adaptive tracking algorithm in occlusion. We decompose the tracking into target translation and scale prediction. A kernelized correlation filter with an adaptive update scheme is adopted to estimate target position. The adaptive online update scheme takes advantage of the confidence score sensitivity to occlusion and reduces the false updating in occlusion during the tracking sequence. The target scale can be estimated by the correlation filter with the ridge regression. Extensive experiments results on 29 challenging occlusion sequences show that the proposed tracking approach achieves the average overlap precision (OP) of 72.2%, which improves the performance by 7.6% compared to the DSST. On OTB-50 dataset, our tracking approach is also superior comparing to several state-of-the-art trackers.
Keywords: Object tracking, correlation filter, Fast Fourier Transform (FFT)
