# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
###### Abstract  
Recent Siamese network-based visual tracking approaches have achieved high performance metrics on numerous recent visual tracking benchmarks, where most of these trackers employ a backbone feature extractor network with a prediction head network for classification and regression tasks. However, there has been a constant trend of employing a larger and complex backbone network and prediction head networks for improved performance, where increased computational load can slow down the overall speed of the tracking algorithm. To address the aforementioned issues, we propose a novel target-aware feature bottleneck module for trackers, where the proposed bottleneck can elicit a target-aware feature in order to obtain a compact feature representation from the backbone network for improved speed and robustness. Our lightweight target-aware bottleneck module attends to the feature representation of the target region to elicit scene-specific information and generate feature-wise modulation weights that can adaptively change the importance of each feature. The proposed tracker is evaluated on large-scale visual tracking datasets, GOT-10k and LaSOT, and we achieve real-time speed in terms of computation and obtain improved accuracy over the baseline tracker algorithm with high performance metrics. visual tracking; model-free tracking; object tracking; bottleneck module; real-time tracking +
Footnote †: journal: Medical  
based on region proposal networks [13; 14], centerness estimation branches [15; 16], and transformer-based feature fusion and estimation [17; 18]. The aforementioned architectural improvements in both feature extractors and prediction heads have shown high accuracy on numerous recently introduced large-scale visual tracking benchmarks [19; 20; 21]. However, along with the recent advancements in the design of feature extractor networks utilizing deeper and wider architectures, prediction head networks have also become more complex, conducting various tasks, such as classification, regression, centerness estimation, and segmentation [22]. To perform these tasks, employing deeper and wider architectures for prediction head networks has brought an increased number of parameters where their effect becomes non-negligible when it comes to real-time tracking speeds, slowing down the overall tracking process. To compress and accelerate deep architectures for efficiency, previous works focus on methodologies, such as network quantization [23], pruning [24; 25], architecture search [26], and more. These aforementioned methods are effective for network acceleration by reducing the number of parameters with minor trade-offs in performance under general classification settings. However, the accelerated networks found by these methods are fixed and permanent under all inputs, lacking the flexibility of adapting to various tasks and scenarios. Because the visual tracking problem can be viewed as a meta-learning problem with few-shot examples [13; 27; 28] to initialize and update the tracker, the tracking framework's adaptiveness and flexibility to encompass various targets and scenes can be considered as a crucial aspect when it comes to handling various tracking scenarios. To address the aforementioned issues regarding the acceleration of deep neural networks for visual tracking, we propose a novel target-aware feature bottleneck module for a Siamese network-based tracking framework. The proposed module receives the information of the target object and its surroundings and can adaptively choose to modulate the relevant features extracted from the backbone feature extractor. The resulting feature representation can improve the discriminability of the target object from its background and is also lightweight, reducing the computational burden for the subsequent tasks performed by the prediction head network. Additionally, instead of attending and modulating all feature channels, we propose a channel group-based approach where this provides a reasonable trade-off between adaptiveness and efficiency.