摘要
目标检测跟踪同步算法通过对视频帧的目标实时检测来达到跟踪的目的,该算法主要是为了维持一个能够在线训练的分类器,把从背景采样的样本作为负样本,从目标区域采样的样本作为正样本,然后通过分类器把二者区分开,以达到跟踪效果。然而当目标产生形变以及目标区域发生遮挡的时候,如何对样本采样和精确标记成为跟踪成败的关键。在结构支持向量机的框架下,提出一种基于结构支持向量机的目标检测跟踪同步算法。由于结构支持向量机的输出可以是复杂的数据结构,因此采用结构支持向量机,把目标位置估计作为结构支持向量机的输出,避免了对样本标记精确估计的需要,克服了当目标发生遮挡和大范围变形时导致的跟踪失败。仿真实验表明,该算法有良好稳定的跟踪效果。
The tracking by detection algorithm implements target tracking by detecting the target in each frames in real time.This algorithm aims to maintain an online training classifier,which intends to separate the target from the background for tracking by taking the samples from the background area as negative samples and from the target area as positive samples.But when the target was sheltered,or the shape of target changed in a large scale,how to sample and mark the samples accurately was critical for success tracking.A tracking by detection algorithm was proposed based on structured Support Vector Machine （SVM）.Since the output of structured SVM can be very complex data structure,the position of the target was taken as the output of the structured SVM,which can overcomes tracking drift problem when the target was sheltered or the shape of target changed greatly.Experimental results show that the proposed algorithm has a good and stable tracking performance.
出处
《电光与控制》
北大核心
2014年第12期49-52,70,共5页
Electronics Optics & Control
关键词
目标跟踪
目标检测
结构学习
支持向量机
target tracking
target detection
structured learning
support vector machine
