基于深度学习的移动机器人目标跟踪系统

针对当前智能移动机器人在跟踪过程中常因目标发生外观形态上的变化而丢失跟踪目标的问题，利用Caffe深度学习框架和ROS机器人操作系统作为开发平台，设计一个高准确度及高实时性的移动机器人目标跟踪系统并进行了研究.使用对于目标形变、视角、轻微遮挡及光照变化具有鲁棒性的基于孪生卷积神经网络的GOTURN目标跟踪算法，通过ROS系统为桥梁使离线训练的跟踪模型实时应用于TurtleBot移动机器人上，并开展了详细的测试.实验结果表明，该目标跟踪系统不仅设计方案可行，实现了移动机器人在各种复杂场景下有效地跟踪目标，还具有成本低、性能高和易扩展等特点.

Target Tracking System for Mobile Robot Based on Deep Learning

In view of the current intelligent mobile robot in the tracking process due to the target shape on the changes in a loss of tracking target, using the Caffe deep learning framework and ROS robot operating system as a development platform, a high accuracy and high real-time target tracking system of mobile robots is designed for research. The GOTURN target tracking algorithm based on the twin convolutional neural network, which is robust to target deformation, viewing angle, slight occlusion and illumination changes is used, and the ROS system is used as a bridge to enable the offline training tracking model to be applied to the TurtleBot mobile robot in real time, also a detailed test is carried out. Experimental results show that the target tracking system is not only feasible in design, but also has the characteristics of low cost, high performance and easy expansion.