Stereo object detection with the applications to mobile robot

Abstract

Robust and accurate object detection are needed for the applications to mobile robots. Unfortunately, most of the existing object detection approaches cannot satisfy with the real application due to either slow speed or lower accuracy. Computer Vision has been increasingly important in enabling smart technologies. In this project, the author aims to develop an object detection system which can provide high accuracy while reducing missing detection by using stereo camera and state-of-the-art machine learning. Stereo vision is one of the options that can be implemented in order to provide more data and parameters besides visuals provided by a single camera. This will involve the use of the disparity map in order to obtain depth information of the scene. By combining the use of depth information with a deep learning framework, fast, robust and accurate stereo object detection can be achieved.