基于像素可信度和空间位置的运动目标跟踪

Moving Object Tracking Based on Location and Confidence of Pixels

运动目标跟踪是视频信息处理的重要研究课题之一·首先将时间域上的中值背景建模与空间域上最小交叉熵法相结合,用于检测运动目标所在跟踪区域·在此基础上,提出了跟踪区域内基于像素的可信度与空间位置的权重函数,利用HSV色彩分布模型计算出目标模型与预测模型间的相似性,选出最优相似模型作为当前目标模型,从而实现了多目标的跟踪·实验显示,该算法计算简单,对相似目标能实现准确的跟踪,对非刚性目标的尺度变化、多目标的交叉及部分遮挡具有鲁棒性·

Moving object tracking is a critical issue of image sequence processing. In this paper, a moving object tracking algorithm based on location and confidence of pixels is proposed. Firstly, the moving objects are detected by combining the median background model in temporal domain with the minimum crossentropy in spatial domain. Then the rectangle area of the objects are obtained, and at the same time an HSV color distribution model is used to measure the similarity between target rectangles and hypothetical rectangles. In this process, a weighting function based on location and confidence of pixels is presented to weigh the pixel values in the rectangle area of the tracking. The experimental results show that the algorithm is computationally efficient and robust to scale invariant, partial occlusion and interactions of nonrigid objects, especially similar objects.