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UC Regents
Description
This dataset contains virtual and real-world examples of object detection association in connected vehicle environments. The main object class that is represented in this dataset is vehicles and these represent the majority of samples within the dataset, though some other classes such as motorcycles, bicycles, trucks, and buses are also present in the dataset. The image data are RoI images produced by a machine learning object detector; every pair of images for each timestep is assigned a positive label ("1") if both RoI images are of the same vehicle, otherwise a negative association label ("0") is assigned to the image pair. There are 10k+ real-world image pairs that have been human labeled as well as over 5 million virtual images pairs that have been labeled by a heuristic automatic labeler. The real-world data was recorded on the UCSD campus using Intel RealSense D415 stereo cameras and the Sighthound vehicle recognition API was used to detect and classify each object. The virtual data was recorded in the Carla Simulator (https://carla.org/) and the object detector used was detectron2. Position estimates are included for the virtual data using information from Carla as well as the object detector (see paper for more details). This work was funded by the UCSD Center for Wireless Communications (CWC) Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Thornton, Samuel; Flowers, Bryse; Dey, Sujit (2023). Data from: Multi-Source Feature Fusion for Object Detection Association in Connected Vehicle Environments. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0HX1CVJ
Type
dataset
Identifier
ark:/20775/bb0795841q
Subject
Data fusion Neural networks (Computer science) Computer vision Digital twin Deep learning Object association Object detection Machine learning Intelligent transportation systems Connected vehicles Vehicular technology Vehicular safety La Jolla (San Diego, Calif.)
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