Detecting occlusion and camouflage during visual tracking

Visual tracking is an important scientific problem; the human visual system is capable of tracking moving objects in a wide variety of situations. It is also of considerable practical importance; many actual and potential applications of visual tracking algorithms exist in domains such as surveillance, medicine, robotics and the media. Although many effective tracking algorithms exist, occlusion and camouflage remain a common problem. These can cause a tracker to become dissociated from its target, so that the data it produces is unrelated to the target´s behaviour. We focus on the detection of occlusion and camouflage during particle filter-based tracking. We use a Gaussian Mixture Model of particle distribution, extracted via the EM algorithm, to investigate the effects of occlusion and camouflage on the particle set representing a given target. The information gained from this investigation informs the design of process-behaviour chart which alerts the tracker of the occurrence of occlusion or camouflage. Data produced by the process-behaviour chart is also used to map out the boundary of the interfering object, providing valuable information about the viewed environment.