MOTorchard

To capture the necessary information, we deployed a stereo camera(ZED 2i, America) on a transport cart. This setup facilitated the acquisition of crucial data including camera poses, point clouds, and Red Green Blue(RGB) images.

To ensure the accuracy and reliability of the dataset, meticulous alignment and calibration processes were taken to align all collected data with synchronized timestamps through.To closely align with practical production requirements, human subjects were selected as the primary targets for detection within the orchard environment in this research. The distribution of obstacles within the orchard and their corresponding motion patterns were classified into distinct categories: cross, cross&return, circle, converge, sim(simulated scenarios that accurately represented real operational conditions).We employed the DarkLabel software for precise MOT annotation in our dataset.