Multiple target tracking for intelligent headlights control

Abstract

Intelligent vehicle lighting systems aim at automatically regulate the headlights' beam angle so as to illuminate as much of the road ahead as possible, while avoiding dazzling other drivers. A key component of such a system is a computer vision software able to distinguish blobs due to vehicles' head and rear-lights from those originating from road lamps and reflective elements like poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging cases not yet solved which hamper the adoption of such a system; notably, faint and tiny blobs corresponding to quite distant vehicles which disappear and reappear now and then. One reason for the errors in the classification is that it was carried out independently of other frames. Hence, we address the problem by tracking blobs in order to 1) obtain more feature measurements per blob along its track, 2) compute motion features, which we deem relevant for the classification and 3) enforce its temporal consistency. This paper focuses on the problem of constructing blob tracks, which is actually one of multiple target tracking, but under special conditions: we have to deal with frequent occlusions as well as blob splitings and mergings. We approach it in a novel way, by formulating the problem as a maximum a posteriori inference on a Markov random field. We present qualitative (in video form) and quantitative results which show that our new tracking method achieves good tracking results with regard to the original objective.