Optimal fuzzy-PID controller design for object tracking

Object tracking is a technique for finding moving objects of interest and estimating their trajectory or path with regard to time in a series of images. It involves object representation, detection, and tracking. It becomes an important field of study due to the need in video surveillance, traffic monitoring, live sport video analysis and many other applications. In this paper, both static camera-based and dynamic camera-based object tracking techniques have been developed. The static camera-based object tracking was developed with NI LabVIEW, and Shape adaptive mean-shift algorithm has been used for tracking. In case of dynamic camera-based object tracking, an optimal Fuzzy-PID controller has been designed to adjust the position of the pan/tilt mechanism so as to trace the object's trajectory. Genetic algorithm (GA) was used to find the optimal values of the operating ranges (scaling factors) of the membership functions. The performance of the system has been tested by different trajectories like step, sinusoidal, circular and elliptical at different frequencies 1, 50 and 100 rad/sec. The system has best performance at low frequencies and when the frequency or speed of the object increases, the system performance decreases which complies for real systems. The simulation results demonstrate that GA tuned Fuzzy-PID controller has given us the best results in terms of reduced steady-state error, faster rise time and settling time, and object position stabilization than PID, Fuzzy and Fuzzy-PID controllers, which shows that optimal Fuzzy-PID controller designed is more appropriate and efficient.