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

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).

• 2020 to 2022

• 2023

• Flowers, Bryse
• Thornton, Samuel

• Dey, Sujit

The data consists of .png files for the images and .npy files for the data labels. For the data generation and Data_Loader.ipynb, we used python 3.9, pytorch 1.10, and numpy 1.21 but any of the same major versions should work as well (i.e. python 3.x, pytorch 1.x, numpy 1.x). Opencv and matplotlib are also used for plotting and viewing images.

This work was funded by the UCSD Center for Wireless Communications (CWC)

• La Jolla (San Diego, Calif.)

• Algorithm: Semi-supervised learning
• Algorithm: Supervised learning
• Algorithm: Unsupervised learning
• Computer vision
• Connected vehicles
• Data fusion
• Deep learning
• Digital twin
• Intelligent transportation systems
• Machine learning
• Neural networks (Computer science)
• Object association
• Object detection
• Task: Binary classification
• Task: Clustering
• Task: Image classification
• Task: Multiclass classification
• Vehicular safety
• Vehicular technology

• data
• software
• text

Identifier: Bryse Flowers: https://orcid.org/0000-0003-1474-4732

Identifier: Samuel Thornton: https://orcid.org/0000-0002-3087-2577

Identifier: Sujit Dey: https://orcid.org/0000-0001-9671-3950

Primary associated publication

• S. Thornton, B. Flowers and S. Dey, "Multi-Source Feature Fusion for Object Detection Association in Connected Vehicle Environments," in IEEE Access, vol. 10, pp. 131841-131854, 2022, https://ieeexplore.ieee.org/document/9982615. https://doi.org/10.1109/ACCESS.2022.3228735

Software

• Carla Simulator (carla.org)

Creative Commons Attribution 4.0 International Public License

• UC Regents

Under copyright (US)

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2024-06-28