Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection
The combination of traditional methods(e.g.,ACF)and Convolutional Neural Networks(CNNs)has achieved great success in pedestrian detection.Despite effectiveness,design of this method is intricate.In this paper,we present an end-to-end network based on Faster R-CNN and neural cascade classifier for pedestrian detection.Different from Faster R-CNN that only makes use of the last convolutional layer,we utilize features from multiple layers and feed them to a neural cascade classifier.Such an architecture favors more low-level features and implements a hard negative mining process in the network.Both of these two factors are important in pedestrian detection.The neural cascade classifier is jointly trained with the Faster R-CNN in our unifying network.The proposed network achieves comparable performance to the stateof-the-art on Caltech pedestrian dataset with a more concise framework and faster processing speed.Meanwhile,the detection result obtained by our method is tighter and more accurate.
Convolutional Neural Network Cascade classifier Faster R-CNN Pedestrian detection
Bei Tong Bin Fan Fuchao Wu
National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing,China
国际会议
第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)
成都
英文
243-257
2016-11-03（万方平台首次上网日期，不代表论文的发表时间）