Many powerful visual tracking techniques exist. Most of them are built using model estimation concepts, such as Kalman filters or Sequential Monte Carlo methods, particularly particle filters. A significant advantage that particle filters have over Kalman filters is that particle filters can represent ambiguity in the targetís position, whereas Kalman filters can only represent a single hypothesis at a time. The multiple hypotheses maintained by particle filters can be viewed as physical particles sprinkled over the image plane, and are often shown as spots of different colours or intensity.
While many effective tracking algorithms have been based on the particle filter, problems remain. Occlusion arises when another object, with different features, e.g. colour, comes between the camera and target. Camouflage occurs when an object with similar features lies behind the target and makes the target invisible from the cameraís point of view. Either of these events can cause a tracker become dissociated from its target, so that the data it produces is unrelated to the targetís behaviour.
Previous approaches to these problems have sought to keep the trackerís particle set more tightly focused on the target. This is done by incorporating more information about the target and/or the environment it is moving through. Multiple models of motion might be used to predict the targetís future location, more detailed texture and colour cues might be used to better model its appearance, or areas of the environment in which the target is more likely to appear might be identified. These approaches, however, do not entirely remove the problems of occlusion and camouflage. As a result, we would argue that the solution lies not in avoiding, but in detecting and reacting to these disruptive events.
In the present paper we focus on the detection of occlusion and camouflage during particle filter-based tracking. We use a clustering algorithm, the Expectation Maximization algorithm, to investigate the distribution of the particles representing a given target during the events of occlusion and camouflage. This clustering process provides vital information about fluctuation in the behaviour of particle sets, which is assessed using a process-behaviour chart. The process-behaviour chart will alert the tracker of the occurrence of occlusion or camouflage. The information produced by the process-behaviour chart is then used by the tracker to map out the boundary of the interfering object, providing valuable information about the viewed environment.