结合运动矢量的分权快速压缩跟踪算法

Fast compressive tracking algorithm based on motion vector and assigning weight value

针对跟踪过程中目标移动过快产生跟踪漂移问题,提出一种结合超像素运动矢量的候选目标位置搜寻策略;在跟踪框架内分块提取特征并根据区域分配置信权值,弱化跟踪框架内边缘背景对分类结果的干扰,提高分类器分类鲁棒性;针对当目标出现严重遮挡时,分类器仍对正负样本特征进行学习而导致的学习不准确问题,提出增加目标遮挡检测机制,避免错误分类,有效解决目标遮挡问题。实验结果表明:提出的算法与当前先进目标跟踪算法相比,效果较好,克服目标快速移动、目标形变、复杂背景干扰、目标遮挡、光线变化等一系列挑战性的跟踪难点,实现目标长时间有效跟踪的同时,跟踪效率满足实时性的要求。

To reduce the drift phenomenon in object tracking, a candidate object location search method was proposed combining motion vector with super pixel. In order to weaken the influence of complex background and improve the tracking robustness, the features from the blocks in the tracking box were assigned different weights according to their locations. The classifier may get wrong information if it continues learning when the tracking object is largely occluded.A object detection approach was proposed to avoid the false classification in the situations of object occlusion. The experiment results show that the proposed algorithm has better performance and can track successfully and efficiently for a long time, compared with some state-of-the-art works in many complicated situations, e.g. swift movement, object deformation, complex background, occlusion and illumination variation.