Scale-invariant histogram of oriented gradients: novel approach for pedestrian detection in multiresolution image dataset

Abstract

This paper proposes a scale-invariant histogram of oriented gradients (SI-HOG) for pedestrian detection. Most of the algorithms for pedestrian detection use the HOG as the basic feature and combine other features with the HOG to form the feature set, which is usually applied with a support vector machine (SVM). Hence, the HOG feature is the most efficient and fundamental feature for pedestrian detection. However, the HOG feature produces feature vectors of different lengths for different image resolutions; thus, the feature vectors are incomparable for the SVM. The proposed method forms a scale-space pyramid wherein the histogram bin is calculated. Thus, the gradient information from all the scales is encapsulated in a single fixed-length feature vector. The proposed method is also combined with color and texture features. The proposed approach is tested on three established benchmark pedestrian datasets: INRIA, NICTA, and Daimler. An improvement of ≥4.5% in the miss rate is achieved for all the three datasets considered. We also show that the SI-HOG can be applied to multiresolution datasets for which the HOG feature cannot be applied. Additionally, the MapReduce model is used to obtain the same outcome. The results indicate that the proposed approach outperforms the pedestrian-detection methods considered in this work.