Synth-Yard-MCMOT-1 – A synthetic multi camera multi object dataset in the context of yard logistics

The dataset "Synth-Yard-MCMOT-1" is a novel image dataset for multi-camera multi-object tracking (MCMOT). It is the first of its kind to be generated in a virtual environment with the main focus on the tracking of trucks in yard logistics environments. The dataset consists of a total of 12,008 images generated by eight different cameras. The images contain 44,232 bounding boxes and segmentation masks and 52 individual tracks. Additionally, we provide a ninth camera, which is used to generate unified ground-truth information for the whole scene from an orthographic, top-down perspective comparable to a bird’s eye or map-view. The purpose of this dataset is to provide yard management systems with relevant data, which can be employed when aiming to determine the exact position of a truck and specifically identifying which gateway or designated parking spot it is located in. Additionally in this dataset we added the annotations in the COCO-style format as well as the raw image-data.Initial benchmarks for single-camera tracking demonstrate a mean IDF1 score of 0.96 and a mean MOTA score of 0.94, laying the baseline for computing world coordinates via MCMOT.This work is part of the project “Silicon Economy Logistics Ecosystem” which is funded by the German Federal Ministry of Transport and Digital Infrastructure. This research has partly been funded by the Federal Ministry of Education and Research of Germany and the state of North-Rhine Westphalia as part of the Lamarr-Institute  for Machine Learning and Artificial Intelligence. We would like to thank our colleagues Julian Hinxlage, Oleg Belov, Habib Kilic, Nicole Wagner-Hanl, Leona Cerimi, Antonia Ponikarov and Nico Lindemann for their support.

The code to create this dataset can be found here: https://git.openlogisticsfoundation.org/silicon-economy/base/ml-toolbox/synth-data-mcmot