用对比学习提升轻量级追踪的辨识力，适合移动端部署。
CFTrack: Enhancing Lightweight Visual Tracking through Contrastive Learning and Feature Matching
- 引入对比特征匹配模块，动态评估目标相似性。
- 在LaSOT等数据集上精度超越多数轻量级追踪器。
- 实测在Jetson NX上达136帧/秒，适合边缘设备使用。
在计算资源受限的移动和边缘设备上实现高效且具备强辨识能力的轻量级视觉追踪仍具挑战。传统轻量级追踪器在遮挡和干扰下鲁棒性不足，而压缩后的深度追踪器则易出现性能下降。为此，我们提出CFTrack，融合对比学习与特征匹配以增强特征表示的辨识能力。该方法通过一个新颖的对比特征匹配模块，结合自适应对比损失进行优化，在预测时动态评估目标相似性，从而提升追踪精度。在LaSOT、OTB100和UAV123上的大量实验表明，CFTrack超越多个先进轻量级追踪器，在NVIDIA Jetson NX平台达到136帧/秒的运行速度。HOOT数据集上的结果进一步验证了其在严重遮挡下的强辨识能力。
原文摘要 · Abstract (English)
Achieving both efficiency and strong discriminative ability in lightweight visual tracking is a challenge, especially on mobile and edge devices with limited computational resources. Conventional lightweight trackers often struggle with robustness under occlusion and interference, while deep trackers, when compressed to meet resource constraints, suffer from performance degradation. To address these issues, we introduce CFTrack, a lightweight tracker that integrates contrastive learning and feature matching to enhance discriminative feature representations. CFTrack dynamically assesses target similarity during prediction through a novel contrastive feature matching module optimized with an adaptive contrastive loss, thereby improving tracking accuracy. Extensive experiments on LaSOT, OTB100, and UAV123 show that CFTrack surpasses many state-of-the-art lightweight trackers, operating at 136 frames per second on the NVIDIA Jetson NX platform. Results on the HOOT dataset further demonstrate CFTrack's strong discriminative ability under heavy occlusion.
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