基于m-best数据关联和小轨迹关联多目标跟踪算法

视频多目标跟踪中目标较多时,联合概率数据关联算法计算量大,实时性差。由于遮挡等问题,联合概率数据关联算法得到的往往是目标的轨迹片段。针对上述问题,首先利用线性规划自适应迭代求解m个最优联合事件简化联合概率数据关联算法,然后提出基于Kalman滤波及外推法的双向运动预测计算轨迹间的距离矩阵,用近邻传播聚类对目标的轨迹片段进行关联。实验结果表明,本文提出的方法在目标多且容易发生遮挡的情况下仍能够实时有效的跟踪,提高了跟踪准确度,具有一定的抗干扰能力。

Multi-target tracking based on m-best data association and tracklet associatio

In the video multi-target tracking, the joint probability data association (JPDA) algorithm involves a potentially huge number terms, which is weak for the real time performance, when the number of the target is large. Moreover, targets are often undetected due to occlusion or other detector failures. The classic JPDA often gets the part trajectory of the objects, not the integrity trajectory. To solve these problems, a method based on m-best JPDA and tracklet association is proposed. Firstly, to reduce the computational complexity, the integer linear program is used to find the m-best hypotheses and simplify the JPDA algorithm. After that, the distances between each target trajectory are computed based on the motion evaluation by Kalman filter and the simply linearly extrapolation. The affinity propagation cluster algorithm is used to merge the tracklet of the object and get the fully trajectories. Experiments show that the proposed method still has the effective and real time performance when the number of target is large and occlusion is easy to happen.