Scalable histogram of oriented gradients for multi-size car detection

This paper addresses two contributions for improving the accuracy and speed of preceding car detection systems. First, it proposes a feature description using Scalable Histogram of Oriented Gradient (SHOG) to solve scale problem of car region on the image. Without resizing the images to a fixed size, it is capable to extract a high-discriminated features with on the same feature space. Second, instead of use sliding window method to obtain candidate regions, it uses laser data information. This mechanism reduce the processing time significantly. In addition, an integral image method is utilized to support fast computation of the feature extraction. For classifying candidate regions into car and non-car class, linear support vector machine (SVM) is performed. The experimental results show that proposed descriptor accuracy is 3% higher than using standard HOG feature.