# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 4 Experiments
### Ablation Study  
To provide a further comprehensive analysis of our TAFT algorithm and our target-aware feature bottleneck module, we perform an ablation analysis of the various challenge attributes for visual tracking and test our module with multiple combinations of the feature channels numbers \(c^{\prime}\) and the number of feature groups \(N_{g}\). For the ablation study, we use the success plots acquired from the LaSOT test split and use the AUC measures for the evaluation and comparison. **Ablations on Challenge Attributes:** To measure the robustness of TAFT on diverse challenge attributes in the tracking task, we visualize and compare the success plots of sequences with eight chosen attributes for the test split of LaSOT in Figure 5. Each test sequence is annotated with one or more challenge attributes, and the number of test sequences annotated with a given challenge attribute is denoted at the top of each plot. Our proposed TAFT achieves notable performance on the eight attributes, and the largest performance gains are obtained for the attributes of rotation, deformation, motion blur, and aspect ratio change. Owing to the target-specific information obtained from the initial  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & **TAFT** & **Chapt-Track** & **D DMP-50** & **ATOM** & **Dissim** & **Sim-BPN+** & **MDNet** & **SIFT** & **Occan** & **CFNet** & **Sim-FC** & **  
frame, our proposed bottleneck module can fully utilize the target-context information and can learn to modulate the relevant channels that can be useful for localizing the target object under these attributes. Additionally, because the baseline tracker GlobalTrack is a motion prior-free global search-based tracking algorithm, it shows weaknesses under the attribute of background clutter when compared to SiamRPN++ and ATOM, where GlobalTrack is more likely to mislabel a similar distractor object in the background as a false positive. Although our tracker TAFT has the same global search-based framework, it achieves higher performance while running at a faster, real-time tracking speed. **Ablations on the Target-Aware Feature Bottleneck Module:** To accurately quantify the effectiveness of the proposed module, we conduct a further ablation analysis for the proposed module and report the AUC metrics of the success plots obtained from the test split of the LaSOT dataset. First, we test our module with six different hyperparameter combinations of the output feature channel numbers \(c^{\prime}\) and feature group numbers \(N_{g}\) where we show the results in Table 3. We denote the reduction in the computational load of the prediction head as \(c^{\prime}/c\), because the computational load is linearly proportional to the number of channels. Setting (1) in the first row is identical to the tracker used to report the performances in Tables 1 and 2. If we reduce the number of feature groups \(N_{g}\) to eight in setting (2), we observe a performance decrease of \(-0.006\). For settings (3) and (4), we use \(c^{\prime}=128\) with a varying number of \(N_{g}\) and observe a similar trend where the performance decreases by \(-0.008\) when \(N_{g}\) is reduced. The same tendency can be observed in settings (5) and (6) where the channel number is further reduced to \(c^{\prime}=64\), and reducing \(N_{g}\) causes a significant decrease in performance by \(-0.024\). From the results in settings (1)-(6), we can speculate that our target-aware feature modulation scheme plays a significant role in improving the target discriminability when tracking, and decreasing the feature group \(N_{g}\) hampers the ability of fine-grained feature modulation of the bottleneck module. Additionally, to quantify the contribution of our proposed bottleneck module, we remove our module entirely in setting (7), where removing the module results in a performance decrease of \(-0.023\), validating the effectiveness of our proposed framework for visual tracking. Figure 5: Success plots evaluated under 8 attributes of LaSOT test split.