Visual vehicles detection and robustness enhancement

This article is about vehicles detection from visual data which is an important part of automotive driving assistance systems. It is a big challenge to make the vehicles detection more robust. In order to enhance the robustness, a lane lines stabilization methods is proposed by studying the imaging model. Next, a hypothesis generation and verification framework is applied for saving time. Finally, our approach is compared to the Entropy-verified method and DPM in runtime performance and false positive ratio. It turns out that our approach gets a balance between runtime performance and false positive ratio.