New Deep Convolutional Neural Network Architecture for Pedestrian Detection
Carlos Ismael Orozco, M. Buemi, Julio Jacobo Berllés · 2017
Pedestrian detection is currently a topic of interest in computer vision due to its applications such as driver assistance systems and surveillance in public spaces, among others. The good results obtained using deep convolutional networks in vision tasks make them an attractive tool to improve the capacities of pedestrian detection systems. In this work we propose a deep convolutional network architecture to classify as pedestrian or non-pedestrian the candidate regions previously generated using a simple pyramidal sliding window approach. A distintive characteristic of the CNN in this system is that it separates pedestrian from non-pedestrian images without the aid of a pre-classification stage, and without the need of special tuning steps or initials conditions, making it more straightforward than other CNN-based solutions.The data used for training and testing come from the Caltech-USA Pedestrian dataset [8, 9]. We have evaluated the classification results on the proposed architecture and have obtained an average of ~ 98% success for the validation set. We have also carried out another evaluation of our system using the Benchmark proposed by Dollάr et.al. [9], and obtained results that are competitive with those mentioned in the bibliography.