People detection in crowded scenes using hierarchical features

We propose a new architecture(based on Faster R-CNN framework) for people detection. Our model extracts the first,third,fifth stage of the VGG16 network to form a robust feature map which consists of both the semantic and localization information.Besides,we replace the fc6 and fc7 layer of the original structure with two convolution layers, since the fully-connected layer is so time-consuming.We finetune our network on the Brainwash dataset,and it's partially initialized with the model trained on the imagenet dataset. The experimental results demonstrate great performance(with AP of 92.4% and recall of 93.5%),which exceeds our baseline methods a lot.