|Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection|
|Tong Bei(童贝); Fan Bin; Wu Fuchao; Tong B(童贝)
|Abstract||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.|
|Keyword||Convolutional Neural Network
|Corresponding Author||Tong B(童贝)|
Tong Bei,Fan Bin,Wu Fuchao,et al. Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection[C],2016.
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