多目标跟踪中基于次模优化的轨迹片段生成方法

作为智能视觉任务的基础工作，多目标跟踪(MOT)一直是计算机视觉领域具有挑战性的课题之一。遮挡是影响跟踪准确性的主要因素，为此该文采用基于检测跟踪的思想，以轨迹片段为基础进行关联获取目标的完整轨迹；同时，为提高跟踪鲁棒性，该文将轨迹片段的生成问题转化为运筹学中的设施选址问题，并进而提出基于次模优化的轨迹片段生成方法。该方法融合梯度(HOG)和颜色(CN)两个互补特征进行目标表征，并根据运动信息设计权重系数提高目标匹配准确度，最后提出具有约束的次模最大化算法实现全局范围内的数据关联生成轨迹片段。通过在多个基准数据集上的对比实验，表明该文算法在保证性能的同时能有效处理遮挡问题。

Tracklet Generation Method by Submodular Optimization for Multi-Object Tracking

As the basis of many intelligent visual tasks, Multi-Object Tracking (MOT) is a challenging problem in computer vision. Occlusion is a main factor affecting the tracking accuracy. To solve the occlusion problem, in this paper, the strategy of tracking-by-detection is adopted to obtain complete trajectories of targets based on associating tracklets. Meanwhile, to improve the tracking robustness, the tracklet generation problem is transformed into the facility location problem in operations research area and further a submodular optimization based tracklet generation method is proposed. In this method, two complementary features including Histogram of Oriented Gradient (HOG)and Color Name (CN) are integrated to describe the target appearance, and a weighting coefficient is also designed by motion information to improve the matching accuracy. At length, a submodular maximization algorithm with constraints is developed to achieve the global data association by selecting the targets to form the tracklets. By comparative experiments on the benchmark datasets, the proposed method can solve the occlusion problem effectively with guaranteed performance.