Abstract

An important issue in tracking is how to incorporate an ap- propriate degree of adaptivity into the observation model. Without any adaptivity, tracking fails when ob ject properties change, for example when illumination changes affect surface colour. Conversely, if an ob- servation model adapts too readily then, during some transient failure of tracking, it is liable to adapt erroneously to some part of the background. The approach proposed here is to adapt selectively, allowing adaptation only during periods when two particular conditions are met: that the ob ject should be both present and in motion. The proposed mechanism for adaptivity is tested here with a foreground colour and motion model. The experimental setting itself is novel in that it uses combined colour and motion observations from a fixed filter bank, with motion used also for initialisation via a Monte Carlo proposal distribution. Adaptation is performed using a stochastic EM algorithm, during periods that meet the conditions above. Tests verify the value of such adaptivity, in that immunity to distraction from clutter of similar colour to the ob ject is considerably enhanced.