Aiming at the problem that Tracking accuracy of Tracking-Learning-Detection (TLD) tracking algorithm decreases when targets are under different light and shade conditions and target scales change, an improved TLD tracking algorithm is proposed. In this paper, Speeded Up Robust Features (SURF) feature point matching method was adopted as the tracking module, and the feature point pairs with low confidence were removed by adding the evaluation of feature point pairs. By introducing Contrast Limited Adaptive Histogram Equalization (CLAHE) into the detection module, a random Circle feature classifier is proposed, and the HOG feature matching method is used to replace the normalized correlation matching method in the nearest neighbor classifier. In addition, the detection range is adjusted adaptively, which reduces the computational complexity and effectively improves the adaptability of the algorithm to multi-scale. Experimental results show that the proposed algorithm can effectively overcome the influence of environmental shading conditions, and has strong robustness to scale changes and high tracking accuracy. Compared with the classical TLD algorithm, the improved algorithm performs better.
In recent years, binary descriptors have attracted more and more attention due to their low memory consumption and high speed. It is well known that these representations are worse than higher-dimensional and histogram-based descriptors such as SIFT. Therefore, this paper proposes a fusion gradient distinction binary image descriptor (GDBID). Gradient comparison is added on the basis of the original gray comparison to enrich the information contained in the descriptor. At the same time, the comparison patches of different sizes are obtained by constructing concentric circles to achieve anti-noise. In addition, a threshold is set to filter patches to reduce the dimension of descriptors. Experimental results show that the GDBID has a precision is close to the best algorithm (SIFT), and the time consumption is lower than the fastest ORB in the literature.
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