SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation

• URL: http://arxiv.org/abs/2307.01024v2
• Date: Sun, 24 Mar 2024 07:10:27 GMT
• Title: SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation
• Authors: Changhong Fu, Liangliang Yao, Haobo Zuo, Guangze Zheng, Jia Pan,
• Abstract summary: Domain adaptation (DA) has demonstrated significant promise for real-time nighttime unmanned aerial vehicle (UAV) tracking. The state-of-the-art (SOTA) DA still lacks the potential object with accurate pixel-level location and boundary to generate the high-quality target domain training sample. This work proposes a novel SAM-powered DA framework for real-time nighttime UAV tracking, i.e., SAM-DA.
• Score: 21.998189930406014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain adaptation (DA) has demonstrated significant promise for real-time nighttime unmanned aerial vehicle (UAV) tracking. However, the state-of-the-art (SOTA) DA still lacks the potential object with accurate pixel-level location and boundary to generate the high-quality target domain training sample. This key issue constrains the transfer learning of the real-time daytime SOTA trackers for challenging nighttime UAV tracking. Recently, the notable Segment Anything Model (SAM) has achieved a remarkable zero-shot generalization ability to discover abundant potential objects due to its huge data-driven training approach. To solve the aforementioned issue, this work proposes a novel SAM-powered DA framework for real-time nighttime UAV tracking, i.e., SAM-DA. Specifically, an innovative SAM-powered target domain training sample swelling is designed to determine enormous high-quality target domain training samples from every single raw nighttime image. This novel one-to-many generation significantly expands the high-quality target domain training sample for DA. Comprehensive experiments on extensive nighttime UAV videos prove the robustness and domain adaptability of SAM-DA for nighttime UAV tracking. Especially, compared to the SOTA DA, SAM-DA can achieve better performance with fewer raw nighttime images, i.e., the fewer-better training. This economized training approach facilitates the quick validation and deployment of algorithms for UAVs. The code is available at https://github.com/vision4robotics/SAM-DA.

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