【摘要】 针对目前图像跟踪器跟踪不稳定、跟踪精度不高及不能满足实时性要求等问题,提出了一种概率加权的质心跟踪算法。该算法首先对波门内的像素进行阈值分割,摒弃灰度低于阈值的背景像素,保留目标像素的灰度值,然后计算波门内目标区域的质心。实验结果表明:基于概率加权的质心跟踪方法能够有效降低复杂背景和噪声干扰,增强跟踪系统的抗干扰能力,减少传统跟踪系统中使用大量灰度梯度值带来的巨大计算量,从而提高跟踪器的精度和稳定性。创新点在于通过引入概率加权的方法,在计算初始时刻的目标质心时使用贝叶斯概率作为权重,而没有设置离散的阈值来分辨目标,减少了传统跟踪系统中使用大量灰度梯度值产生的计算复杂度。更多还原

【Abstract】 The probability weighted centroid tracking(PWC) algorithm was proposed to solve the problems of the image tracker,such as the low tracking precision,instability and could not meet the requirement of real time. Firstly,the threshold segmentation of the pixels in wave-gate was processed by the algorithm.These pixels whose gray levels were lower than the counterparts of background were discarded . The gray values of targets pixels were retained. And then the centroid coordinates of wave-gate in the targets regions were obtained. The experimental results demonstrate that PWC can effectively decrease the background clutter and noise countermeasure as well as reduce huge calculation and computation complexity due to computing multiple graduations of gray levels in traditional tracking methods of tracker. Furthermore,it can also increase the ability of anti-countermeasure and greatly improve the tracking precision and stability of the tracker. The Bayesian probability was introduced and regarded as the weight when calculating the initiative time of the target centroid.更多还原