Abstract
Visual object trackers based on correlation filters have recently demonstrated substantial robustness to challenging conditions with variations in illumination and motion blur. Nonetheless, the models depend strongly on the spatial layout and are highly sensitive to deformation, scale, and occlusion. As presented and discussed in this paper, the colour attributes are combined due to their complementary characteristics to handle variations in shape well. In addition, a novel approach for robust scale estimation is proposed for mitigatinge the problems caused by fast motion and scale variations. Moreover, feedback from high-confidence tracking results was also utilized to prevent model corruption. The evaluation results for our tracker demonstrate that it performed outstandingly in terms of both precision and accuracy with enhancements of approximately 25% and 49%, respectively, in authoritative benchmarks compared to those for other popular correlation- filter-based trackers. Finally, the proposed tracker has demonstrated strong robustness, which has enabled online object tracking under various scenarios at a real-time frame rate of approximately 65 frames per second (FPS).
Highlights
Robust visual object tracking has been attracting substandtial attention
All test sequences of object tracking benchmark (OTB) have been tagged with 11 attributes which represent challenging conditions in various scenarios, such as background clutters (BC), motion blur (MB), illumination variation (IV), in-plane rotation (IPR), low resolution (LR), occlusion (OCC), out-of-plane rotation (OPR), scale variation (SV), deformation (DEF), fast motion (FM) and out-of-view (OV)
A novel tracker is proposed for overcoming challenges such as deformation, scale variation, and occlusion in the field of visual tracking
Summary
Robust visual object tracking has been attracting substandtial attention. It is a significant problem in computer vision, as evidenced by its numerous implementations in robotics, services, monitoring, and human-machine interaction. The second type is used to exploit the features that are extracted from a deep convolutional neural networks (CNN) [18,19,20,21] , which is trained either online or on recognition datasets These approaches can substantially improve the performance, the utilization of more complicated tracking algorithms or features would enormously increase the computational complexity, which might render the model unsuitable for real-time visual object tracking. In the development of trackers that are based on CF, the discrimination performance should be improved and the real-time performance requirement should be satisfied Due to their strong feature representation performances, CNNs have realized significant success on visual tracking tasks and in many other scenarios. The high-confidence mechanisms in (21) and (22) are used to judge whether updating model in the current frame is necessary for the prevention of model corruption
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