CSVD: a cross-scenario vehicle dataset for multi-object tracking

Leveraging visual sensing technologies for the detection and tracking of vehicles represents a critical application domain for unmanned aerial vehicles (UAVs), notably in challenging operational contexts. This study focuses on enhancing UAV functionalities in intricate environments through the development of a specialized dataset, derived from battlefield scenarios, to facilitate advanced research on vehicle detection and multi-target tracking under complex conditions. A comprehensive collection of vehicular movement videos spanning diverse scenarios was amassed and manually annotated, culminating in the creation of the " Cross-Scenario Vehicle Detection" (CSVD) dataset. This dataset encompasses a wide array of environmental settings, featuring urban landscapes, plains, and forests, across the four seasons, resulting in a total of 13,025 meticulously annotated images. Utilizing several state-of-the-art deep learning models, we established robust benchmarks for object detection. Additionally, an extensive evaluation and performance validation were conducted using cutting-edge multi-object tracking algorithms on the CSVD dataset, incorporating diverse assessment metrics. The conducted experiments demonstrate the dataset's robust applicability and versatility, endorsing its effectiveness for the development and evaluation of UAV-based vehicle detection and multi-target tracking systems in complex settings.