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2 November 2018 A scale adaptive tracker based on point feature
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In this paper, we propose a algorithm to tracking target using point feature. The point feature is extracted from the pixels in the first frame and used to label the pixels in the next frame as belonging to either target or background. The positive and negative samples are extracted from the pixels of target and surrounding background, and used to train several weak classifiers, which combine to build a strong classifier using AdaBoost algorithm. The negative samples are given the greater weights than positive samples, which is to avoid that a large number of pixels in background are labeled incorrectly. To efficiently learn a large number of samples, the adopted weak classifier is a linear perceptron model, which is trained and updated using stochastic gradient descent. Only the dot-product between matrices and the sum of matrix elements need to be calculated. To distinguish the similar targets, the histogram-based mean shift algorithm is applied to eliminate those wrong image patches. The histogram of target will be updated over the time. The experiment results show that the proposed algorithm can estimate scale better when scale change, posture change and occlusion occurs.
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Hao Sun, Xiaoping Yang, Zhihong Chen, and Jinafei Li "A scale adaptive tracker based on point feature", Proc. SPIE 10817, Optoelectronic Imaging and Multimedia Technology V, 108170V (2 November 2018);

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