基于Mask R-CNN的多目标跟踪算法

Multiple Object Tracking Algorithm Based on Mask R-CNN

针对目前多目标跟踪算法在面对目标频繁遮挡时跟踪效果较差的问题,提出采用Mask R-CNN作为检测器,根据检测结果利用Kalman滤波器预测下帧图像中跟踪目标的位置,用改进匈牙利算法进行数据关联,并利用轨迹修正方案应对轨迹中断问题.将该算法在MOT16数据集的各测试集上进行实验,实验结果表明,该算法目标跟踪准确率为55.1%,且针对目标被遮挡问题效果较好.

Aiming at the problem that the current multiple object tracking algorithm had poor tracking effect in the face of frequent target occlusion,using Mask R-CNN as a detector,according to the detection results,the Kalman filter was used to predict the position of the tracking target in the next frame image and the improved Hungarian algorithm was used for data association,and the trajectory correction scheme was used to deal with the problem of trajectory interruption.The algorithm was experimented on each test set of MOT16 dataset,the experimental results show that the tracking accuracy of the algorithm is 55.1%,and the effect of target occlusion is better.