Multi-perspective Traffic Video Recording

Abstract:  This dataset was produced as part of the NANCY project (https://nancy-project.eu/), with the aim of using it in the fields of communication and computer vision. Within the dataset are contained three different scenarios, each of which has three videos. All three videos were captured by different devices; a vehicle-mounted unit, a roadside unit (RSU), and a drone. These devices were placed at different locations, with different angles and heights but all facing the same area of interest, providing multiple different views of the desired location. The present architecture offers the integration of complementary data, offering a richer and more comprehensive understanding of the observed area. Additionally, the dataset was tailored made for use in semantic communications (SemCom). By using SemCom the above dataset provides a more precise and enriched understanding of the area of interest. This is achieved by transmitting only meaningful and context-aware information rather than raw data, significantly reducing data traffic while at the same time enhancing the quality of insights.The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456. Instructions: The present dataset, through the multiple number of devices that it contains, has the ability to offer a wide range of applications of its data. By applying the global perspective offered by the vehicle unit, RSU, and drone data, the dataset offers innovative solutions in areas such as object detection, activity recognition, environmental monitoring, and artificial intelligence (AI). Moreover, it is an ideal solution for the development and demonstration of SemCom's advantages, in which context-aware data transmission and analysis can enhance efficiency and precision. In conclusion, the rich, multi-angle observations make this dataset a valuable resource for tackling complex challenges in modern computer vision, AI, and communication systems.