# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 3 Methods
### Detection Based on MNet
#### 3.3.2 MNet  
Balls in sports videos are small objects. The backbone network for small object feature extraction needs to consider multiple aspects, such as the amount of calculation, the receptive field, and the abstraction level of the features. The feature level continues to become abstract as the network deepens, and the receptive field will also become larger with convolution and pooling. However, as the amount of calculation continues to increase, the spatial location information is weakened. During the convolution operation, a certain level of features often only uses the same specification of convolution, and the capture of local context semantics is relatively weak. Therefore, multiple groups of parallel convolutions are used to capture the local semantics in different areas, and then mutual fusion forms a richer comprehensive semantics. To sum up, we redesigned the multiscale feature enhancement backbone network (MNet) from the perspectives of multiscale feature fusion, context learning, and integration. Its core modules are the MC Block and Upsample Block. The improved backbone network structure is shown in Figure 3.  
Multiscale feature enhancement and local contextual feature fusion block (MC Block) is the feature extraction module in the backbone network of this chapter, which can increase the receptive field and complete local context fusion. As shown in Figure 3b, the MC Block first uses a down sampling convolution to reduce the size of the feature map to reduce the amount of calculation, and then we use a set of dilated [34] convolutions with expansion rates of 1 with different steps \(s\in\{1,2,3,4\}\) to process feature map. The convolution of each branch can be regarded as focusing on the local feature map of different area sizes. Among them, the stride \(s=1\) convolution is the basic branch, and the \(s>1\) branch is the enhancement branch. To facilitate concatenation, we establish corresponding padding values across distinct branches. Subsequently, we concatenate the feature maps generated by multiple branches that possess identical dimensions. The concatenation of multiple branches can be regarded as the fusion of local contexts of different scales. Since we use dilated convolution, the model does not bring additional weight parameters while increasing the receptive field, so the backbone network will become relatively lightweight. After the features of multiple branches are spliced, convolution is used to reduce the dimensionality, and then residual connections are performed in the form of residual blocksin ResNet. Note that when the downsampling part and enhancement branch are removed, the MC Block degenerates into the classic residual block structure. The upsampling module restores the feature map to its original resolution size. We use a simple bilinear interpolation method to increase the resolution and then use a convolution with a stride of 1 to add details.  
The improved backbone network focuses on the detection of small objects. Compared with other backbone networks, there are fewer downsampling steps. Therefore, with the same input size, MNet has the largest output space size, which brings a certain performance improvement. Secondly, MNet uses dilated convolution to balance the network depth and receptive field and limit the scale of model parameters.