基于灰色预测的均值漂移跟踪器视觉目标跟踪

Mingming Lv, Li Wang, Yuan-long Hou, Q. Gao, Run-min Hou
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引用次数: 2

摘要

本文提出了一种嵌入灰色预测的均值漂移跟踪器,用于视觉目标跟踪。灰色模型[GM(1,1)]作为灰色预测的基本模型,用于在历史信息较少的情况下进行目标位置预测。将预测的位置作为MS迭代的初始点,而不是原始MS跟踪器中的先前跟踪结果。为了减少计算量,对GM(1,1)的预测方程进行了简化,遮挡程度由Bhattacharyya系数和设定的阈值确定。如果超过一定程度,则MS迭代可能不会收敛到真实结果,将目标位置替换为预测位置,以防止故障跟踪。实验结果表明,该方法能有效地解决运动速度快、遮挡严重的问题,且性能优于原MS跟踪器和带粒子滤波的MS跟踪器。
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Mean Shift Tracker With Grey Prediction for Visual Object Tracking
In this paper, the mean shift (MS) tracker embedded with grey prediction is proposed for visual object tracking. As the basic model of grey prediction, grey model [GM(1,1)] is employed to predict object location with few historical information. The predicted location is taken as the initial point of MS iteration instead of the previous tracking result in the original MS tracker. The prediction equation of GM(1,1) is simplified to reduce computation, and the occlusion degree is determined by the Bhattacharyya coefficient and a set threshold. If the degree exceeds a certain limit, the MS iteration may not converge to the true result and the object location is replaced with the predicted location to prevent failure tracking. The experimental results show that the proposed approach can effectively deal with the problems of fast-moving and serious occlusion and has a better performance than the original MS tracker and the MS tracker with particle filter.
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期刊介绍: The Canadian Journal of Electrical and Computer Engineering (ISSN-0840-8688), issued quarterly, has been publishing high-quality refereed scientific papers in all areas of electrical and computer engineering since 1976
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