关键词: 视觉跟踪, 目标丢失判别机制, 实时多域卷积神经网络, 重叠度, 出镜头

Abstract:

When the tracked target is severely occluded or leaves the field of camera’s view, the robot tracking target is often lost. In order to track the target robustly, a target loss discrimination tracking algorithm named YOLO-RTM is proposed. This method detects the target in the first frame of video through YOLOv3. Then the Real-Time Multi-Domain convolutional neural Network（Real-Time MDNet, RT-MDNet） tracking algorithm is used to predict the change of the target frame by frame. Finally, the Intersection over Union（IoU） is calculated, and the updating mode of the model is determined according to the comparison result of the IoU and the preset threshold value. If the IoU is higher than the threshold value, RT-MDNet is used to update the appearance model. If the IoU is lower than the threshold value, Yolov3 is used to search the target again and update the appearance model. The experimental results on the Turtlebot2 robot show that the reliability of mobile robot tracking in general scenes is satisfied by the proposed algorithm, and the practicability of tracking algorithm is improved effectively.

Key words: visual tracking, target loss discrimination mechanism, real-time multi-domain convolutional neural networks, Intersection over Union（IoU）, leaves the field of camera’s view