Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD - Chenghang Wang; Xiaochun Shen; Zhaoxiang Zhang; Chengyang Tao; Yuelei Xu (Drones)
Multi-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During drone flight, factors such as high-altitude environments, small target proportions, irregular target movement, and frequent occlusions complicate the multi-object tracking task. This paper proposes a cross-scene multi-object tracking (CST) method to address these challenges. Firstly, a lightweight object detection framework is proposed to optimize key sub-tasks by integrating multi-dimensional temporal and spatial information. Secondly, trajectory prediction is achieved through the implementation of Model-Agnostic Meta-Learning, enhancing adaptability to dynamic environments. Thirdly, re-identification is facilitated using Dempster–Shafer Theory, which effectively manages uncertainties in target recognition by incorporating aircraft state information. Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields.