一种利用时空约束轨迹片段关联的目标跟踪方法

摘要

Noise segmentation, partial and full object occlusion may result in tracking failure. In order to solve these problems, proposed an object tracking algorithm by associating tracklets with fundamental spatio-temporal constraints. First detect moving objects, then generate tracklet, and grow these tracklets to find the best spatial and temporal association of observations. Experiments prove the proposed method can successfully track multiple moving vehicles and persons under occlusion,noisy detections and split-merge situations.%由于检测不准确,目标部分或整体遮挡会造成检测失败.针对以上问题,提出一种利用时空约束的轨迹片段关联方法实现对目标的跟踪.通过减背景方法检测到移动目标,生成轨迹片断,通过计算轨迹片段的时空连续性关联轨迹片段,找到最符合时空连续性的轨迹片段关联.通过实验证明,该方法可以有效解决遮挡、误检测以及目标合并分离问题.