# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 3 Proposed Method
### Incorporation of the Target-Aware Feature Bottleneck Module
For \(h_{z}\) and \(h_{x}\), we use a differentiable, lightweight neural network composed of convolution, global average pooling, and linear layers with ReLU in between, where all the layers except for the last layer are shared between networks \(h_{z}\) and \(h_{x}\). Using the output parameters from \(h_{z}\) and \(h_{x}\), the input feature maps \(F_{z}\) and \(F_{x}\) go through the three aforementioned stages as in  
\[ \begin{split}{F_{z}}^{\prime}=\alpha_{z}^{q}\odot\sigma(\alpha_{ z}^{p}\odot F_{z}\oplus\beta_{z}^{p})\oplus\beta_{z}^{q}\\ {F_{x}}^{\prime}=\alpha_{x}^{q}\odot\sigma(\alpha_{x}^{p}\odot F_{ x}\oplus\beta_{x}^{p})\oplus\beta_{x}^{q},\end{split}  \tag{9}\]  
where \(\sigma(\cdot)\) represents a \(1\times 1\) convolution layer with unit stride, and \(\odot\) and \(\oplus\) are group-wise multiplication and addition operators that appropriately broadcast the parameters to the channels of each group. Because the affine transformation parameters are obtained conditioned on the target context feature, the tracker can adaptively change these parameters according to the given scene, where the tracker can keep its discriminative capacity even under high compression. Please refer to Figure 3 for an overall view of the framework and operation of the target-aware bottleneck and Algorithm 1 for the step-by-step operation of our proposed tracker with the integrated target-adaptive feature bottleneck module. ```
Input :Sequence of length \(T\), with RGB frame images \(\{I_{0},I_{1},I_{2},\ldots,I_{T-1}\}\)  Bounding box coordinates of the target at the initial frame \(b_{0}\) Output :Bounding box coordinates of the target at time \(t\), \(b_{t}\) for all frames # Initialization and target-adaptive bottleneck feature extraction at time \(t=0\)  Compute query feature map \(F_{z}=\phi(I_{0})\) using feature extractor \(\phi(\cdot)\)  Obtain spatially pooled target context representation \(z^{\prime}\) using \(b^{\prime}_{0}\) by ROIAlign, as in Equation (7)  Compute feature modulation parameters as in Equation (8) and store in memory  Apply modulation to \(F_{z}\) to obtain lightweight \({F_{z}}^{\prime}\) as in Equation (9)  Obtain spatially pooled target feature representation \(z\) using \(b_{0}\) by ROIAlign and store in memory # For later frames in the video sequence, \(t>0\) for\(t=1\) to \(T-1\)do # Region proposal stage  Compute search feature map \(F_{x}=\phi(I_{t})\) using feature extractor \(\phi\)  Using the modulation parameters found in Equation (8), apply modulation to \(F_{x}\) to obtain lightweight \({F_{x}}^{\prime}\) as in Equation (9)  Perform depth-wise cross correlation with \(z\) to obtain correlation map \(\hat{x}\) as in Equation (1)  Obtain top-\(N\) ROI boxes \(\{\hat{b}_{1},\ldots,\hat{b}_{N}\}\) using region proposal network Equation (2) # Region pooling and classification stage  Obtain spatially pooled candidate feature representations \(\{x_{1},\ldots,x_{N}\}\) using candidate boxes using ROIAlign  Correlate feature representations \(x_{i}\) and \(z\) to obtain \(\hat{x}_{i}\) as in Equation (3)  Perform ROI-wise region classification and box refinement using region classification network as in Equations (4) and (5)  Choose a refined ROI \(\hat{b}^{\prime}_{k}\) with the highest classification score as final output of the tracker for frame \(t\) end
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**Algorithm 1** Visual tracking with target-aware feature bottleneck