Companied Edge Flow Histogram and Color Histogram to Represent Tracking Objects

Abstract

Tracking moving objects from moving platform in videos sequence is a challenging task .object movement and platform movement are sources of variations in scene. Mean Shift Algorithm (MSA) is the common tracking algorithm due simple and efficient procedure. Correct Background Weight Histogram(CBWH)decrease background effect in target representation module. The main drawbacks in MSA is the ineffective model representation to handle illumination variation and occultation problems. MSA failed to track objects in video containing wide ranges of variations and background motion. In this paper motion information is exploited from edge flow by gradient differential. Histogram for edge flow combined with color histogram called Motion Flow Histograms (MFH).MFH used to represent tracking target between two successive frames. New module target representation reduces the false tracker rate without evident increasing time compare to the classical tracking MSA and CBWH.