# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 4 Experiments
### Qualitative Evaluations and Analysis  
In this section, we provide further visualizations of the qualitative results produced by TAFT, on the test split of LaSOT. We show the bounding box output comparisons with other tracking algorithms in Figure 6, where we show the output of our tracker with ATOM, GlobalTrack, SPLT, SiamRPN++, and VITAL. The results from five example videos, _zebra-17, squirrel-13, horse-15, bottle-14_, and _crab-3,_ are shown in each row, where the output bounding boxes for each tracker can be distinguished by its respective color shown in the bottom of the figure. While other tracking algorithms suffer from the drift issue arising from the challenges of distractor objects of a similar appearance and category as in _zebra-17_ and _bottle-14_, occlusion from other objects as in _squirrel-13_ and _crab-3_, and the deformation of the target as in _horse-15_, our proposed TAFT can successfully track the target object under these challenging circumstances.  
Furthermore, we present additional visualizations for the target-adaptive feature modulation parameters in Figure 7, where group-wise multiplier values \(\alpha_{z}^{q}\) and \(\alpha_{x}^{q}\), which are applied to the feature groups as in Equation (9), are shown on ten example frame image pairs \((I_{z},I_{x})\) under the network hyperparameter setting (1) in Table 3. We observe that multiplier values can change adaptively given the target and its surroundings, and our target-aware feature bottleneck module is trained to utilize different feature groups in the lightweight search feature map \(F_{x}{}^{\prime}\) and the query feature map \(F_{z}{}^{\prime}\). Output multiplier values vary greatly between channel groups, based on the factor of the query or search patch, target object class, target object appearance, and distractor objects in the background.  
Based on the aforementioned quantitative and qualitative experimental results, we discuss the weaknesses of our proposed method and possible future directions for improvements. Although we observed that our proposed bottleneck module shows strengths in the attributes of various challenging conditions in the large-scale long-term tracking benchmark LaSOT, our tracking framework is less competitive on the short-term benchmark of GOT-10k. This is due to the global search characteristic of our tracking framework, where the tracker looks for the target outside the vicinity of the previous bounding box. Although a global search strategy removes the possibility of long drifts caused by the restricted search area, it can cause more brief localization errors. Possible future directions can include hybrid search strategies where both global and local searches are employed for inferring smoother trajectories and an adaptive search area size based on the confidence of the baseline tracker.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Setting** & \(e^{\prime}\) & \(N_{g}\) & \(e^{\prime}/e\) & **AUC** \\ \hline (1) & 256 & 16 & 1/2 & 0.555 \\ (2) & 256 & 8 & 1/2 & 0.549 \\ (3) & 128 & 16 & 1/4 & 0.546 \\ (4) & 128 & 4 & 1/4 & 0.538 \\ (5) & 64 & 32 & 1/8 & 0.543 \\ (6) & 64 & 4 & 1/8 & 0.519 \\ (7) & 256 & - & 1/2 & 0.532 Figure 6: Qualitative evaluation of the test split of LaSOT. Tracker outputs for videos _zebra-17, squirrel-13, horse-15, bottle-14,_ and _crab-3_. Best viewed zoomed in.