A Perception-Based Interpretation of the Kernel-Based Object Tracking

This paper investigates the advantages of using simple rules of human perception in object tracking. Specifically, human visual perception (HVP) will be used in the definition of both target features and the similarity metric to be used for detecting the target in subsequent frames. Luminance and contrast will play a crucial role in the definition of target features, whereas recent advances in the relations between some classical concepts of information theory and the way human eye codes image information will be used in the definition of the similarity metric. The use of HVP rules in a well known object tracking algorithm, allows us to increase its efficacy in following the target and to considerably reduce the computational cost of the whole tracking process. Some tests also show the stability and the robustness of a perception-based object tracking algorithm also in the presence of other moving elements or target occlusion for few subsequent frames.