Two Parallel Deep Convolutional Neural Networks for pedestrian detection

Abstract

Pedestrian detection attracts lots of attentions in the field of computer vision in recent years. It is difficult to handle data imbalance between positive and negative examples and easy-to-confused negative samples for pedestrian detection when training a single deep convolutional neural network (CNN) model. In this paper, we present a deep learning approach that combines two parallel deep CNN models for pedestrian detection. We propose using two deep CNNs, and each of which is capable of solving a particular mission-oriented task to form parallel classification models. Then, the models are integrated to build a more robust pedestrian detector. Experimental results on the Caltech dataset demonstrate the effectiveness of our approach for pedestrian detection compared to other state-of-the-art deep CNN methods.