Vision-Based Autonomous Human Tracking Mobile Robot

Theint Theint Htwe, Wut Yi Win, Lei Lei Tun Shwe

Abstract


Tracking moving objects is one of the most important but problematic features of motion analysis and understanding. In order to effectively interact robots with people in close proximity, the systems must first be able to detect, track, and follow people. Following a human with a mobile robot arises in many different service robotic applications. This paper proposes to build an autonomous human tracking mobile robot which can solve the occlusion problem during tracking. The robot can make human tracking efficiently by analysing the information obtained from a camera which is equipped on the top of the robot. The system performs human detection by using Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) algorithms and then uses HSV (Hue Saturation Value) color system for detecting stranger. If the detected human is stranger, robot will make tracking. During the process, the robot needs to track the stranger without missing. So, Kalman filter is used to solve this problem. Kalman filter can estimate the target human when the human is occluded with walls or something. This paper describes the processing results and experimental results of a mobile robot which will track unmarked human efficiently and handle the occlusion using vision sensor and Kalman filter.


Keywords


camera; human tracking; HOG; HSV; Kalman filter; SVM.

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References


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