| Abstract: |
This paper presents a powerful vehicle detection technique employing a novel scene-specific sliding windows strategy. Unlike conventional approaches focusing on only appearance characteristics of vehicles, the proposed detection method also utilizes actually observable size-patterns of vehicles in a road. In our work, good data to train the size-patterns, i.e., size information of non-interacting moving-blobs are first collected based on the developed blob-level analysis technique. Then, a new region-wise sequential clustering algorithm is performed to train and maintain the size-pattern model, which is utilized to deform shapes of the sliding windows scenespecifically at each image position. All the proposed procedures operate full-automatically in real-time without any assumptions, and allow us to achieve more accurate and computationally efficient detection of vehicles in multiple scales and aspect-ratios. In the experiments on the real-time highway system, we found that
performance of the proposed method is excellent in the aspects of detection accuracy and processing time. |