SAMViTrack: A Search-Region Adaptive Mamba-ViT Tracker for Real-Time UAV Tracking

Achieving fast and robust object tracking is critical for real-time Unmanned Aerial Vehicle (UAV) applications, where targets often move unpredictably and environmental conditions can rapidly change. In this paper, we propose the Search-Region Adaptive Mamba-ViT Tracker (SAMViTrack), a novel framework that combines the efficiency of Mamba attention with the powerful feature extraction capabilities of Vision Transformer (ViT). Our tracker dynamically adjusts the search region based on the target's motion and environmental context, ensuring precise tracking even under challenging conditions such as occlusions, fast motion, and scale variations. By integrating an adaptive search mechanism, our SAMViTrack significantly reduces computational overhead without compromising accuracy, making it suitable for real-time deployment on UAVs with limited onboard resources. Extensive experiments on benchmark datasets demonstrate that our method outperforms both traditional and modern trackers, achieving superior accuracy and robustness with improved efficiency. The proposed tracker sets a new baseline, especially by combining Mamba and ViT, for UAV tracking by offering a balance between speed, accuracy, and adaptability in dynamic environments.