适用于复杂场景的多目标跟踪算法

在视觉目标跟踪应用中,传统算法在光照变化、阴影、遮挡和背景运动等复杂环境下面临着鲁棒性较差的问题。针对上述问题,首先在局部二进制类型(LBP)背景模型基础上,提出了自适应像素距离阈值编码的背景模型(ST-LBSP)克服阴影和光照变化对目标检测的影响;其次,为了克服遮挡、背景运动等问题,计算图像块之间的最小像素距离和图像块和目标历史数据之间的标准化差值平方和距离,依据距离信息对图像块进行整合;同时,对整合后的图像块,计算其光流矢量、区域内差异性和区域间差异性,对图像块进行分割;最后,采用结构化的支持向量机设计多目标跟踪器,实现了鲁棒的视觉跟踪。基于标准数据集的实验结果显示,该方法具有较强的鲁棒性和较高的跟踪精度。

Multiple object tracking algorithm for the complex scenario

In the application of object tacking,there are some typical problemsin complex scenario,such as light changing, shadow, occupancy and moving background. To overcome light changing and shadow, the algorithm of shadow tolerant local binary similarity pattern is proposed, which is based on the local binary pattern background model. Then, the distance discriminator which is calculated between the current target bounding box and the target bounding box in history is utilized to solve the problems of occupancy and moving background. The distance discriminator is also adopted to detect target fragments. Based on this detection result, the merging procedure can be achieved. After merging, the optical flow of each target bounding box is calculated.The differencesin a block and amongblocks are calculated. The bounding box segmentation procedure is processed based on the calculated similarity information. Finally, the structure support vector machineis used to design the target association procedure. Experimental results on multiple target tracking benchmark show that the proposed algorithm can achieve excellent tracking precision and robustness.