A Context-Aware Adaptive Framework for UAV-Based Target Detection and Tracking

Abstract

Unmanned Aerial Vehicles (UAVs) have become critical platforms for missions such as surveillance, reconnaissance, and target tracking, which require real-time decision-making, reliable sensing, and efficient resource utilization. However, limited onboard computing capacity, energy constraints, variable terrain conditions, and situations where targets are partially or fully obscured limit the performance of traditional fixed-configuration sensing and tracking approaches. In this study, we propose a context-aware and adaptive UAV-based target detection and tracking framework that dynamically selects the most appropriate detection and tracking algorithm by jointly evaluating terrain characteristics and mission requirements. The proposed system includes a three-stage terrain analysis module supported by HSV color space filtering, Canny edge detection, Laplacian texture variance, and contrast-based features. In cases where color-based classification is insufficient, Random Forest-based classification is used to distinguish between vegetation, bare ground, and urban areas; the terrain classification model achieves approximately 90% accuracy during the training and testing process. In the target detection phase, a YOLOv11-based model was trained on a specialized tank dataset created from various sources and labeled in YOLO format, achieving an mAP50 performance of approximately 85%. In the tracking phase, single-object and multi-object tracking algorithms are selected via a scoring-based decision mechanism depending on the terrain type and mission scenario. Additionally, a hybrid anomaly detection mechanism that evaluates target loss, sudden bounding box changes, and view inconsistencies was integrated into the system, thereby enhancing tracking reliability and enabling the re-detection or algorithm switching process when necessary. Experimental results demonstrate that the proposed context-aware approach can reduce computational load while maintaining tracking robustness under various environmental conditions. These findings highlight that environmental awareness and adaptive algorithm selection can make significant contributions to autonomy, operational efficiency, and real-time reliability in UAV-based imaging systems.