CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection
Tong Bei(童贝); Fan Bin; Wu Fuchao; Tong B(童贝)
2016-10
Conference Name全国模式识别学术会议(CCPR)
Conference Date2016年11月
Conference Place四川省成都市电子科技大学图书馆求实厅
AbstractThe 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.
KeywordConvolutional Neural Network Cascade Classifier Faster R-cnn Pedestrian Detection
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14472
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorTong B(童贝)
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Tong Bei,Fan Bin,Wu Fuchao,et al. Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection[C],2016.
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