Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection

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	Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection
	Tong Bei(童贝); Fan Bin; Wu Fuchao; Tong B(童贝)
	2016-10
会议日期	2016年11月
会议地点	四川省成都市电子科技大学图书馆求实厅
关键词	Convolutional Neural Network Cascade Classifier Faster R-cnn Pedestrian Detection
英文摘要	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 state-of-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.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/14472]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者	Tong B(童贝)
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Tong Bei,Fan Bin,Wu Fuchao,et al. Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection[C]. 见:. 四川省成都市电子科技大学图书馆求实厅. 2016年11月.