Visual tracking with online assessment and improved sampling strategy
journal contributionposted on 02.03.2020 by Meng Ding, Wen-Hua Chen, Li Wei, Yun-Feng Cao, Zhou-Yu Zhang
Any type of content formally published in an academic journal, usually following a peer-review process.
The kernelized correlation filter (KCF) is one of the most successful trackers in computer vision today. However its performance may be significantly degraded in a wide range of challenging conditions such as occlusion and out of view. For many applications, particularly safety critical applications (e.g. autonomous driving), it is of profound importance to have consistent and reliable performance during all the operation conditions. This paper addresses this issue of the KCF based trackers by the introduction of two novel modules, namely online assessment of response map, and a strategy of combining cyclically shifted sampling with random sampling in deep feature space. A method of online assessment of response map is proposed to evaluate the tracking performance by constructing a 2-D Gaussian estimation model. Then a strategy of combining cyclically shifted sampling with random sampling in deep feature space is presented to improve the tracking performance when the tracking performance is assessed to be unreliable based on the response map. Therefore, the module of online assessment can be regarded as the trigger for the second module. Experiments verify the tracking performance is significantly improved particularly in challenging conditions as demonstrated by both quantitative and qualitative comparisons of the proposed tracking algorithm with the state-of-the-art tracking algorithms on OTB-2013 and OTB-2015 datasets.
National Natural Science Foundation of China under Grant 61673211, Grant U1633105, and Grant 61203170
Aeronautical Science Foundation of China under Grant 20155152041 and Grant 20170752008
Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 18KJB590002
Open Funds for Key Laboratory of Civil Aircraft Health Monitoring and Intelligent Maintenance under Grant NJ2018012
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering