Scale-invariant visual tracking by particle filtering

Abstract

Visual tracking is an important task that has received a lot of attention in recent years. Robust generic tracking tools are of major interest for applications ranging from surveillance and security to image guided surgery. In these applications, the objects of interest may be translated and scaled. We present here an algorithm that uses scaled normalized cross-correlation matching as the likelihood within the particle filtering framework. We do not need color and contour cues in our algorithm. Experimental results with constant rectangular templates show that the method is reliable for noisy and cluttered scenarios, and provides accurate and smooth trajectories in cases of target translation and scaling.