In this article, a system for tracking human's position and orientation in indoor environment was developed utilizing environmental cameras. The system consists of cameras installed in the environment at fixed locations and orientations, called environmental cameras, and a moving robot which mounts a camera, called moving camera. The environmental cameras detect the location and direction of each person in the space, as well as the position of the moving robot. The robot is then controlled to move and follow the person's movement based on the person's location and orientation, mimicking the act of moving camera tracking his/her face. The number of cameras needed to cover the area of the experiment, as well as each camera's position and orientation, was obtained by using particle swarm optimization algorithm. Sensor fusion among multiple cameras is done by simple weighted averaging based on distance and knowledge of the number of robots being used. Xbox Kinect sensors and a miniature quadrotor were used to implement the system. The tracking experiment was done with one person walking and rotating in the area. The result shows that the proposed system can track the person and quadrotor within the degree of 10 cm , and the quadrotor can follow the person's movement as desired. At least one camera was guaranteed to be tracking the person and the quadrotor at any time, with the minimum number of two for tracking the person and only a few moments that only one camera was tracking the quadrotor.

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This paper proposes a human face tracking system for obtaining elderly people's facial images, which can be used to estimate their individual emotion. Quadrotors are used to overcome occlusion and obtain closer facial images, while Kinect sensors provide human detection and quadrotor navigation. Noise from the measured head position results in vibration of the goal position, and subsequently the quadrotor, which can cause blurred images and safety problem. In order to improve the stability of the quadrotor, we propose an algorithm using threshold to fix the quadrotor's goal position. Performance of the algorithm is evaluated by using the detected positions of the quadrotor and is compared with tracking without threshold algorithm, as well as with different threshold values. Based on these positions, face tracking results are also calculated by simulating projection of the face in real world onto the image plane and evaluating the quality of the obtained face.

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