# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 3 Proposed Method
### Implementation Details  
In this section, we describe additional training details on the training and implementation of our proposed algorithm, where we clarify the training datasets, hyperparameter selections, and further architectural specifications. For the training datasets, we employ training splits of the LaSOT [19], GOT-10k [20], ImageNetVID [65], and YoutubeBB [66] video datasets, where we use a subset of held-out sequences from the LaSOT benchmark as the validation split. To sample a training image pair of query and search frames, we randomly choose a video sequence from a large-scale video dataset where an individual sequence is sampled from a uniform distribution. For a sampled image pair \((I_{z},I_{x})\) where all values are normalized to the range \([0,1]\), random data augmentation of additive Gaussian noise (\(\sigma=0.05\)), color jittering (\(\sigma=0.025\)), Gaussian blur (random kernel size of \(\{3,5,7,9\}\)), and horizontal flip are performed with an individual probability of \(p=0.2\) for improved generalization capabilities. After the data augmentation, channel-wise normalization is performed on the input frames using the identical scheme used in ResNet [6], where the mean \(\mu=[0.485,0.456,0.406]\) and standard deviation \(\sigma=[0.229,0.224,0.225]\) are used as in \((I-\mu)/\sigma\). To extract feature representation from the input frames, we employ ResNet-18 [6] for the feature extractor backbone \(\phi(\cdot)\) because it requires relatively less computation compared to other trackers with heavier backbones [10,11,18] while achieving  
Figure 3: Overview of the target-aware feature bottleneck module. moderate performance on various downstream tasks. The input frames are resized to \(W=666,H=400\) and are fed to \(\phi(\cdot)\) while retaining the original aspect ratio where the longest edge of the input image is matched with the output height/width, and zero padding is applied to the right or bottom side of the shorter edge. The output feature maps from \(\phi(\cdot)\) have dimensions of \(h=25,w=42,c=512\), where their channel dimensions are reduced to \(c^{\prime}\) to produce lightweight feature maps. From the feature maps, a pooled target feature \(z^{\prime}\) with a spatial dimension of \(s=5\) is obtained, and the correlated feature map \(\tilde{x}\) is processed through the region proposal network branches \(f_{C}^{P},f_{R}^{P}\), where they both consist of two \(3\times 3\) convolution layers and a final \(1\times 1\) convolution layer with ReLU nonlinearity and group normalization [62] layers with the group number \(G=16\) in between. The channel dimension of the intermediate representations are kept to \(c^{\prime}\). With our proposed feature bottleneck module, the number of channels reduces from \(c=512\) to \(c^{\prime}\), where \(c^{\prime}\) can vary between different settings, and we test our method using the values \(c^{\prime}\in\{256,128,64\}\), each corresponding to a computational reduction rate of \(\frac{1}{2},\frac{1}{4},\frac{1}{8}\). We obtain the total of the top-\(N=64\) candidate ROI boxes by applying NMS with a threshold of 0.9 and spatially pool the ROI features with a spatial dimension of \(s=5\). Afterward, the region classification network branches \(f_{D}^{Q},f_{S}^{Q}\) process the correlated candidate feature representations, where both branches also consist of two \(3\times 3\) convolution layers and a final \(1\times 1\) convolution layer with ReLU nonlinearity and group normalization layers with a group number \(G=16\) in between. Regarding our target-aware feature bottleneck module, it operates on the target context representation \(z^{\prime}\) which is pooled using a dilated initial target bounding box with a context hyperparameter of \(\gamma_{c}=2\) and feature representation with a spatial size of \(s^{\prime}=9\). The input is processed using network branches \(h_{z},h_{x}\) where they share two convolution layers with ReLU and group normalization layers, with a channel compression rate of 4 when processing the intermediate representations.