Histogram of Oriented Gradients Feature Extraction From Raw Bayer Pattern Images
Wei Zhou, Shengyu Gao, Ling Zhang, Xin Lou
Abstract
This brief studies the redundancy in the image processing pipeline for histogram of oriented gradients (HOG) feature extraction. The impact of demosaicing on the extracted HOG features is analyzed and experimented. It is shown that by taking advantage of the inter-channel correlation of natural images, the HOG features can be directly extracted from the Bayer pattern images with proper gamma compression. Due to the elimination of the image processing pipeline, the power consumption and computational complexity of the detection system can be significantly reduced. Experimental results show that the Bayer pattern image-based HOG features can be used in pedestrian detection systems with little performance degradation.