Lane Detection Based on Visual Attention

Vision based lane detection is an essential task in both autonomous lane vehicles research and active safety system development. Hitherto, lane detection is, however, still a challenging issue due to the complexity of the real road scenes. In this paper, we consider lane detection as a visual attention problem. With a Bayesian attention framework, we address the issue from three perspectives: first, lane markings are assumed to be salient in the road scenes, which will pop out driven by the low level features combining with a bottom-up attention mechanism, second, the target-related features are designed guided by a top-down attention strategy, third, the location prior of the lane markings is also investigated. The experimental results show that the proposed lane detection approach is efficient and robust in real scenes.