An Efficient and Robust Human Classification Algorithm using Finite Frequencies Probing

This paper describes a periodicity motion detection based object classification algorithm for infrared videos. Given a detected and tracked object, the goal is to analyze the periodic signature of its motion pattern. We propose an efficient and robust solution, which is related to the frequency estimation in speech recognition. Periodic reference functions are correlated with the video signal. Experimental results for both infrared and visible videos acquired by ground-based as well as airborne moving sensors are presented.