Abstract
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.
Highlights
Visual tracking has been studied over several decades, it is still an active research topic in the field of computer vision and pattern recognition [1], [2]
Motivated by the above observations, this paper firstly proposes a method of online assessment of response map in the framework of the kernelized correlation filter (KCF), and proposes a strategy that combines cyclically shifted with random sampling in deep feature space
If the threshold of ε is chosen to be large, real-time performance of the algorithm is significantly reduced since the strategy of combining cyclically shifted with random sampling in deep feature space is employed quite frequently
Summary
Visual tracking has been studied over several decades, it is still an active research topic in the field of computer vision and pattern recognition [1], [2]. Visual tracking has been found application in a wide ranges, such as intelligent transportation systems (ITS) [3], vision-based navigation [4], surveillance [5] and motion recognition [6], it still remains challenging in the presence of spatiotemporal variation of targets such as occlusion, illumination variation, and out of view. We believe a promising approach to enhance the tracking robustness of the existing algorithms is to first construct evaluation mechanism for online monitoring the tracking performance, and improve the tracking performance when it is not reliable by modifying the tracking algorithm as appropriate. This motivate the research reported in this paper
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