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 e...Show MoreMetadata
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.
Date of Conference: 23-24 November 2015
Date Added to IEEE Xplore: 01 December 2016
ISBN Information:
Electronic ISSN: 2151-2205