Deep CNN-based Visual Target Tracking System Relying on Monocular Image Sensing

Abstract

The one-on-one target tracking problem is important in robot vision. Previous studies mainly focused on locating, depth information and control mechanism. In this study, we construct an autonomously visual tracking system called learn-to-track (LtT) by using a novel approach. This system only depends on a monocular camera. The main component is a deep convolutional neural network called the LtT, which trains a supervised image classifier by using images captured by the monocular camera in the follower robot. By operating merely on two adjacent frames, the network can predict the estimated velocity of the target, i.e., the velocity control for the follower. To verify the effectiveness of the LtT system, we construct a large-scale dataset that supports download l in the simulator, in which the LtT network is trained and the LtT system performance is evaluated. Furthermore, a remarkable tracking performance is achieved.