Abstract
Object tracking is a challenging research task because of drastic appearance changes of the target and a lack of training samples. Most online learning trackers are hampered by complications, e.g., drifting problem under occlusion, being out of view, or fast motion. In this paper, a real-time object tracking algorithm termed “robust sum of template and pixel-wise learners” (rStaple) is proposed to address those problems. It combines multi-feature correlation filters with a color histogram. Firstly, we extract a combination of specific features from the searching area around the target and then merge feature channels to train a translation correlation filter online. Secondly, the target state is determined by a discriminating mechanism, wherein the model update procedure stops when the target is occluded or out of view, and re-activated when the target re-appears. In addition, by calculating the color histogram score in the searching area, a significant enhancement is adopted for the score map. The target position can be estimated by combining the enhanced color histogram score with the correlation filter response map. Finally, a scale filter is trained for multi-scale detection to obtain the final tracking result. Extensive experimental results on a large benchmark dataset demonstrates that the proposed rStaple is superior to several state-of-the-art algorithms in terms of accuracy and efficiency.
Highlights
Object tracking has been widely used in the field of computer vision [1], such as in automatic driving, precision guidance, and video surveillance
Sci. 2020, 10, 3021 termed robust sum of template and pixel-wise learners (Staple), is proposed. It can simultaneously improve tracking accuracy while maintaining real-time performance based on the foundation in Staple of a combination of correlation filters and a color histogram, which is inherently robust to both color changes and deformations
Compared to the baseline Staple, the distance precision rate increases by 6.6%, the overlap success rate increases by 4.9%, and the average center location error increases from 32 to 18.9 pixels according to the results
Summary
Object tracking has been widely used in the field of computer vision [1], such as in automatic driving, precision guidance, and video surveillance. Sci. 2020, 10, 3021 termed robust Staple (rStaple), is proposed It can simultaneously improve tracking accuracy while maintaining real-time performance based on the foundation in Staple of a combination of correlation filters and a color histogram, which is inherently robust to both color changes and deformations. Staple adopts a scheme to update learned filters each frame for handling the target appearance variations over time. Such a scheme tends to bring about model drift due to occlusion or being out of view. We adopt a detection mechanism which can effectively detect despite severe temporary occlusion or when the target is missing in the current frame In this way, the correlation filters terminate the model update.
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