Efficiency Improvement in the Extraction of Histogram Oriented Gradient Feature for Human Detection Using Selective Histogram Bins and PCA

Abstract

Histogram of Oriented Gradient (HOG) feature which was originally proposed by Dalal and Triggs is widely used in vision-based human detection. However, HOG feature extraction method produced a large feature pool which is computationally intensive and very time consuming, causing it not so suitable for real time application. This paper proposed a method to reduce the HOG feature extraction time without affecting too much on its detection performance. The proposed method performs feature extraction using selective number of histogram bins. Higher number of histogram bins which can extract more detailed orientation information is applied on the regions of image that may contain human figure. The rest of the regions in the image are extracted using lower number of histogram bins. This will reduce the feature size without compromising too much on the performance. To further reduce the feature size, Principal Component Analysis (PCA) is used to rank the features and select only the representative features. A linear SVM classifier is used to evaluate the performance of the proposed method. Experiment was conducted using the INRIA human dataset. The test results showed that the proposed method is able to reduce the feature extraction time by 2.6 times compared to the original HOG and 7 times compared to the LBP method while providing comparable detection performance.