一种基于随机场多运动目标跟踪算法

多运动目标跟踪是智能视频监控系统的重要内容。由于目标数量众多且目标之间相互遮挡,会造成跟踪轨迹不连续,这是多目标跟踪问题的难点,也是当前流行算法无法解决的研究的问题。利用目标对相互之间的位置信息受到外界干扰较少的特点,提出基于条件随机场多目标跟踪。与以前针对所有目标的外形特征不同,算法对跟踪子对进行标示。采用分层结构,由低阶状态作为输入序列到高阶,通过代价函数最小化,从而得到最终连续跟踪轨迹。能有效解决复杂环境下多目标不连续的问题。算法对于外形类似的被跟踪目标以及相互遮挡严重的情况有比较明显的优势,与当前优秀的算法相比,实验表明本文算法在定量分析和定性分析上均有较好的结果。

New multi target tracking algorithm based on conditional random field

Multi-target tracking is very important in intelligent video surveillance system.Occlusions among many moving objects and objects with similar appearance that could result in incontinuous trajectory are challenging problems in this field.Based on the fact that the relative positions remain stable between two close pairs,a novel multi-target tracking algorithm based on conditional random field is presented in this paper.Unlike previous approaches which focus only on appearance and motion models for all targets,we consider discriminative features for distinguishing difficult pairs of targets with sets of labels.Multi-level is adopted by this means,in which the set of tracklets produced by previous level is used as an input.And tracking problem is transformed into an energy minimization problem including a set of unary function for a continuous trajectory and a set of pairwise function for pairs of tracklets.This new method is more powerful in deal with objects with similar appearance and badly occlusions compared to state-of-art methods.Qualitative and quantitative experimental results show that this new method has a better performance.