Real-time pedestrian detection using a support vector machine and stixel information

Pedestrian detection systems have become a crucial topic due to their contribution to numerous applications, such as surveillance, self-driving cars, tracking systems, and robotics. Therefore, we have developed an efficient pedestrian detection algorithm using a support vector machine (SVM) and a histogram of oriented gradients (HOG) feature. This paper introduces a system that is capable of reducing the set of candidate detection regions and classifying pedestrians and non-pedestrians in urban traffic by using the stixel world and HOG + SVM, respectively. The stixel world computation in this proposal is used to compute the region of interest of the input image. We also introduce our new human dataset, including more than 3100 human images, and a method used for classifying the dataset. By implementing on a CPU only, we reach high-quality detection at 56 fps in urban traffic scenarios. Our experimental results demonstrate the effectiveness of reducing the set of detection regions to improve the processing time of our system.