Fast Pedestrian Detection Based on HOG-PCA and Gentle AdaBoost
Jiafu Jiang, Hui Xiong · 2012
Pedestrian detection is a major difficulty in the field of object detection. In order to achieve a balance between speed and accuracy, we propose a new framework in pedestrian detection based on HOG-PCA and Gentle AdaBoost. Firstly, each block-based feature of the image is encoded using the histograms of oriented gradients (HOG), then Principal Components Analysis (PCA) is used to reduce the dimensions of the HOG feature set. In the end, Gentle AdaBoost is used to classify the fabric defects. HOG-PCA descriptors can reduce the complexity of computation in contrast to the state-of-the-art algorithms. The Gentle AdaBoost is used to train the pedestrian classifier can improve the efficiency of training. Experimental results demonstrate the robust of our proposed algorithm.