Exploiting clutter: Negative information for enhanced extended object tracking

When tracking an extended object, traditional approaches exploit information only from measurements that are assumed to stem from the target, and discard observations assumed to have been generated elsewhere. However, the fact that these observations were received contains valuable information about where the target is not. This information, which is usually treated as clutter with little value, can also be exploited in order to improve estimation results. This becomes particularly important in situations with low measurement quality or occlusions, where positive observations from the target may be scarce. In these cases, negative observations, which show where the target cannot be, become highly valuable. In this paper, we introduce Silhouette Models, which are able to incorporate information from both types of observations. The benefits of this approach, which include more robust results and resistance to occlusion, are confirmed in the evaluation.