# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 3 Main Architecture
### Detector and Tracker
#### 3.1.1 Head Detector  
In the head detection task, we need to generate the head bounding box for each pedestrian. The overall structure of our head detector is shown in Figure 3. It is an end-to-end two-stage detector, which consists of four functional modules.  
Resnet50 with FPN. First, Resnet50 [53] was used as the backbone network, coupled with feature pyramid networks (FPNs) to extract multiscale features. In this scenario, FPNs downsampled gradually through a bottom-up operation under the effect of Resnet to obtain C1-C4, and then gradually upsampled M1-M4 through a top-down operation, and used the prediction heads to obtain multiple predictions with the same dimension and different sizes.  
CSPM Model. Next, consider that there are many similarities between head detection and face detection tasks. For example, the shapes of the target bounding boxes are similar (approximately a square), and the differences between the targets' appearance features are small. Therefore, both tasks have the difficulty of being easily confused among targets. For this, our method used a context-sensitive prediction module (SCPM) [54] derived from a face detection method named PyramidBox [55]. Inspired by Inception-ResNet [56], it took the predictions from the previous FPN module as input, and had multiple convolutions working in parallel, which were implemented by SSH [57] and DSSD [58]. SSH increased the receptive field of the model by configuring more and wider convolutional prediction models in parallel before other convolutional layers, which is the embodiment of Inception. DSSD added a residual block to each prediction module to increase the depth of the model, which was considered from the perspective of Resnet. The introduction of SSH and  
Figure 3: The architecture of the head detector in our proposal.  
DSSD enhanced the model prediction module from the perspective of breadth and depth respectively, making it more capable of capturing wider and deeper feature information.  
Transpose Convolutions. Then, we performed a transposed convolution [59] operation on the features of all pyramid levels. The convolution operation is essentially a downsampling operation. After the image passes through several convolution layers, a tensor is obtained, and its size is generally smaller than the size of the original image. Although the transposed convolution is essentially an upsampling operation, which could be considered the reverse operation of the convolution, it can be used to increase the size of the tensor and improve the spatial resolution of the feature mapping.  
RPN and two heads. Finally, we used a region proposal network (RPN) [12] to generate target region proposals. RPN consists of four steps: generate anchors that may have targets, use Softmax classifier to identify the positive anchors, use bounding box regression to fine tune the selected positive anchors, and generate proposals through the proposal layer. Finally, the regression and classification heads are used to provide position offsets and target class confidence scores, respectively.