Spatial Context-Aware UAV Target Tracking for Consumer Electronics

Unlike traditional target tracking tasks, UAV tracking scenarios in consumer electronics applications present complex challenges such as background interference, target occlusion, and aerial perspective change, which directly affect tracking performance. To tackle these challenges, we propose FCADtrack, a Spatial Context Awareness-Based Adaptive Real-Time UAV Tracking method for consumer electronics. FCADtrack focuses on fine-tuning to meet UAV-specific requirements and introduces a spatial information interaction mechanism to enhance tracking robustness. First, it incorporates an Adapter Fine-Tuning Module into the ViT backbone, trained on UAV-specific datasets, enabling the model to capture distinctive UAV tracking features and improve adaptability in complex scenarios. Additionally, the Feature Enhanced Contextual Attention (FECA) module is introduced, comprising the Local Spatial Feature Enhanced Module (FEM) and the Global Spatial-Channel Attention Module (SCAM). The FEM extracts rich target features and suppresses background interference through diverse convolutional operations, while the SCAM enhances global feature representation by integrating spatial and channel information using Global Average Pooling (GAP) and Global Max Pooling (GMP). These components significantly boost target feature representation and reduce background interference. The proposed FCADtrack has achieved competitive tracking performance compared with existing methods. Furthermore, it runs a real-time processing speed of 30.3 FPS on the Jetson Orin NX16 platform, demonstrating its potential for real-time consumer electronics applications. Code is released at: https://github.com/qin490/FCADtracker