Recent transformer-based visual tracking models have demonstrated superior performance. However, prior works have been resource-intensive, requiring massive GPU training hours. This resource demand renders them unsuitable for real-world applications. In this paper, we present DETRack, a training-friendly visual object tracking framework that can integrate motion priors by learning. Our framework utilizes an efficient encoder-decoder structure, with the deformable transformer decoder serving as a target head. We introduce a denoising training strategy to simulate historical predictions and enrich the supervision signal during training. Comprehensive experiments confirm the effectiveness and efficiency of our proposed method. Notably, it only takes 11 hours to train DETRack on a single RTX2080Ti, achieving comparable performance on multiple benchmarks to advanced trackers while maintaining a high running speed.

The recent advancements in transformer-based visual trackers have led to significant progress, attributed to their strong modeling capabilities. However, as performance improves, running latency correspondingly increases, presenting a challenge for real-time robotics applications, especially on edge devices with computational constraints. In response to this, we introduce LiteTrack, an efficient transformer-based tracking model optimized for high-speed operations across various devices. It achieves a more favorable trade-off between accuracy and efficiency than the other lightweight trackers. The main innovations of LiteTrack encompass: 1) asynchronous feature extraction and interaction between the template and search region for better feature fushion and cutting redundant computation, and 2) pruning encoder layers from a heavy tracker to refine the balnace between performance and speed. As an example, our fastest variant, LiteTrack-B4, achieves 65.2% AO on the GOT-10k benchmark, surpassing all preceding efficient trackers, while running over 100 fps with ONNX on the Jetson Orin NX edge device. Moreover, our LiteTrack-B9 reaches competitive 72.2% AO on GOT-10k and 82.4% AUC on TrackingNet, and operates at 171 fps on an NVIDIA 2080Ti GPU. The code and demo materials will be available at https://github.com/TsingWei/LiteTrack.

Single-object tracking generally advances by incrementally determining the tracked target's position through interactions between the search region and the template. However, the template provides less information than does the search region in terms of both temporal cues and spatial resolution. To alleviate this imbalance, we introduce an anthropic tracking framework, MATrack (Mutual Affinity Tracker), which explicitly strengthens weak template information and implicitly reduces background clutter through interactions between multiple templates and the search region. Additionally, we propose a coarse-to-fine localization approach that combines the benefits of corner-based and center-based methods. This approach enables us to simultaneously update the most recent state and background information without two-stage training. MATrack achieves state-of-the-art performance on multiple test benchmarks, including GOT-10k, LASOT, TrackingNet, OTB-100, UAV123, and NFS30. Among these benchmarks, MATrack-320's performance stands out, particularly in the short-term tracking dataset GOT-10k, where it achieves an accuracy overlap (AO) of 77.3. We also conduct comprehensive quantitative and qualitative evaluations to demonstrate that our method significantly outperforms other state-of-the-art approaches.