Tracking and classification of arbitrary objects with bottom-up/top-down detection

Recently, the introduction of dense, long-range 3D sensors has facilitated tracking of arbitrary objects. Especially in the context of autonomous driving, other traffic participants driving the streets usually stay well-segmented from each other. In contrast, pedestrians or bicyclists do not always stay on the road and they often get close to static structure of the environment, e.g. traffic lights or signs, bushes, parking cars etc. These objects are not as easy to segment, often resulting in an under-segmentation of the scene and wrong tracking results. This paper addresses the problem of tracking moving objects that are hard to segment from their static surroundings by utilizing top-down knowledge about the geometry of existing tracks during segmentation. This includes methods for discerning static from moving objects to reduce the rate of false positive tracks as well as a classification of tracks into pedestrian, bicyclist, motor bike, passenger car, van and truck classes by considering an objects appearance and motion history. The proposed tracking system is experimentally validated in challenging real-world inner-city traffic scenes.