Synth- Yard-MCMOT - Synthetically Generated Multi-Camera Multi-Object Tracking Dataset In Yard Logistics

This work proposes a novel image dataset for multicamera multi-object tracking and a framework that allows users to generate similar datasets. The dataset, called Synth-YardMCMOT-1, 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. The purpose of the repository is to enable researches to create unique usecase-specific multi-camera tracking datasets with the included dataset-generation pipeline. Initial benchmarks for single-camera tracking demonstrate a mean identification F1 score score of 0.96 and a mean multiple object tracking accuracy score of 0.94, laying the baseline for computing world coordinates via multicamera multi-object tracking.