  Robustly tracking a person of interest in the crowd with a robotic platform
is one of the cornerstones of human-robot interaction. The robot platform which
is limited by the computational power, rapid movements, and occlusions of the
target requires an efficient and robust framework to perform tracking. This
paper proposes a deep learning framework for tracking a person using a mobile
robot with a stereo camera. The proposed system detects a person based on its
head, then utilizes the low-cost, high-speed regression network-based tracker
to track the person of interest in real-time. The visual servoing of the mobile
robot has been designed using a PID controller which utilizes tracker output
and depth estimation of the person in subsequent frames, hence providing smooth
and adaptive movement of the robot based on target movement. The proposed
system has been tested in a real environment, thus proving its effectiveness.
