A fast image enhancement algorithm for highway tunnel pedestrian detection

Abstract

Pedestrian detection is a necessary means of support in modern traffic management. The error and miss detection rate of traditional pedestrian detection are high due to uneven illumination, dim environment in the tunnel, and the blurred monitored image, which makes it difficult for the subsequent identification. Therefore, in this paper, a fast image enhancement algorithm based on imaging model constraint is proposed and narrowed to the pedestrian ROI in the pavement near the street under the scene of highway tunnel. First, the method uses the combination of global atmospheric light and partitioned atmospheric light to estimate the local atmospheric light. Second, transmission is estimated based on the formula derived from the imaging model constraints. Third, the method uses constant instead of illumination to balance tunnel image illumination. Last, the tunnel image is enhanced according to the imaging model. Furthermore, because of the algorithm's real-time requirement, we propose a narrowing region method to thoroughly improve the overall computing efficiency. Considering about the characteristics of high way tunnel, which is a blurred scene and has difficulty recognizing the foreground from the background, we adopt a method of multi-feature integration to detect the enhanced image. Experimental and comparative analysis results show that the proposed method can rapidly and effectively enhance the tunnel image, and improve the effect of pedestrian detection in high way.