Pedestrian object detection with fusion of visual attention mechanism and semantic computation

In response to the problem that the primary visual features are difficult to effectively address pedestrian detection in complex scenes, we present a method to improve pedestrian detection using a visual attention mechanism with semantic computation. After determining a saliency map with a visual attention mechanism, we can calculate saliency maps for human skin and the human head-shoulders. Using a Laplacian pyramid, the static visual attention model is established to obtain a total saliency map and then complete pedestrian detection. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on the INRIA dataset with 92.78% pedestrian detection accuracy at a very competitive time cost.