Digital-Twin-Driven Multivehicle Multidrones Tracking Method

With the development of 6G wireless networks, non-fixed monitoring methods utilizing drone networks as monitoring carriers have been receiving increasing attention. However, due to significant environmental interference affecting the hovering of drones in the air, the video signals often contain substantial random interference, which impacts the performance of current multi-object and multi-camera tracking (MOMCT) algorithms. To address this issue, this paper proposes a digital-twin-driven multi-vehicle multi-drone monitoring system, in which a simulation scenario consistent with the actual environment is created. Furthermore, a large amount of vehicle monitoring video data from multiple drone perspectives with random interference is generated, thus compensating for the shortage of multi-camera vehicle monitoring data, especially data affected by random interference. Additionally, an online tracking framework for MOMCT based on the digital twin system is set up, forming a pipeline that fully utilizes the simulated data generated by the digital-twin system for training and testing. Using this pipeline, we find that a feature detection algorithm combining AttentionNet-CBAM and ResNext101-IBN-A can effectively enhance the ability to identify target features, thereby achieving better multi-vehicle tracking results. Experimental results on the CityFlow dataset verify that the proposed method has improved the IDF1 by 1.52% and IDR by 3.58% in comparison with the state-of-the-art online MOMCT methods.