Pedestrian Detection Based on Visual Saliency and Supervised Learning

Pedestrian detection is a key issue in computer vision, which received extensive attentions. Supervised learning methods with feature extraction and classification are widely used in the pedestrian detection. This paper proposed a pedestrian detection method based on visual saliency and supervised learning. The LC algorithm is used to calculate the saliency value of each training image, followed by the LBP feature extraction. The saliency LBP features and HOG features are combined together as the input of SVM classifier to detect pedestrians. Experimental results show that this method is more accurate and efficient compared with the traditional HOG and LBP feature fusion based method.