## 2 Related Works
### Deep Learning-Based Pedestrian Detection  
As an effective feature learning method, deep learning has made breakthrough in applications such as computer vision, data mining, and speech recognition. In recent years, researchers have conducted in-depth research on pedestrian detection based on deep learning and have achieved abundant results. DBN-Isol (Isolated Deep Belief Network) [15] was proposed for part detection, which performs well on pedestrian detection in the presence of occlusion. ConvNet [19] consists of three convolutional layers, in which features obtained by the second and third layer are fused as input to the fully connected layer to finish pedestrian detection. DBN-Mut (Mutual Deep Belief Network) [20] extended the mutual visibility based on DBN-ISOL, which focuses on the situation where a pedestrian is partially blocked by another pedestrian during pedestrian detection. Unified Deep Networks (UDN) [21] was constructedbased on CNN (Convolutional Neural Network), part detection, a deformation model, and visibility reasoning. It makes full use of the advantages of DBN-ISOL and DBN-Mut, and it combines CNN and BP (Back Propagation) deep networks for pedestrian detection. SDN [22] combined feature learning, saliency mapping, and mixed feature representation in a cascade structure, in which a switchable RBM (Restricted Boltzmann Machine) layer is introduced on the traditional CNN to selectively combine different features. The above methods usually include two or three layers in the deep net.  
To learn more sophisticate features, researchers implemented deeper networks to extract features, which usually include at least 16 layers. UDN was extend to a very deep net by using VGG16 and fast RCNN to obtain the features of different body parts for pedestrian detection [23]. RPN (Region Proposal Network) and faster RCNN (Regions with CNN features) were used simultaneously to detect and segment pedestrians in an image [24]; the backbone network is VGG16. PCN [25] uses an LSTM (Long Short-Term Memory) for part semantic learning, an RPN for region proposals, and a Maxout of different adaptive scale selection and the backbone network is also VGG16. By employing an attention network to the baseline faster RCNN, better detection was obtained [26]. [PERSON] et al. [27] extended the RPN (Region Proposal Network) + BF (Boosted Forest) framework [28] with handcrafted features and CNN features. ROI pooling is applied both on handcrafted features and CNN features for pedestrian candidate proposal. Extracted features are concatenated and sent to BF for classification. [PERSON] et al. [29] implemented a cascaded-phase design with a backbone network of VGG16 for progressive region proposal and pedestrian detection other than independent detection procedures with ROI systems. [PERSON] et al. [30] and [PERSON] et al. [31] employed ResNet with 50 layers for pedestrian feature extraction and obtained state-of-the-art results. [PERSON] et al. [32] utilized deep feature (feature maps from ResNet-50) pyramids for multi-resolution feature extraction and realized a competitive accuracy and real-time pedestrian detection on Geforce GTX GPU. [PERSON] et al. [33] also utilized multi-resolution features from ResNet; the features are concatenated simultaneously for center point location regression and scale prediction without region proposal ahead.  
However, in real scenarios of auto driving, the hardware computing ability is limited. Networks with too many layers are not practical, so a shallow network such as UDN is worth consideration.