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Dataset . 2022
License: CC BY
Data sources: Datacite
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https://doi.org/10.5281/zenodo...
Dataset . 2022
License: CC BY
Data sources: Sygma
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Container spreader pose tracking dataset

Authors: Lourakis, Manolis; Pateraki, Maria;

Container spreader pose tracking dataset

Abstract

This dataset contains image sequences that feature a moving quay crane spreader in a port environment while unloading a container cargo vessel. A container crane spreader is a device that is installed on a crane and used to lift containers after attaching onto them. The sequences were acquired from a viewpoint similar to that of the crane operator using a camera installed next to the operator’s cabin at a height of approximately 20 meters above the quay. The camera thus moves with the crane, resulting in a non-stationary image background. The dataset is organized into several RAR archives, one for each sequence. In addition to the undistorted image frames, it includes for every sequence a text file whose each line consists of the frame id for every image, the spreader’s bounding box and the spreader’s 6D pose (Rodrigues vector for the orientation, and the translation vector). The axis-aligned 2D bounding box is in the format x0 y0 w h where (x0, y0) is the top left corner and w x h its size, all in pixels. The spreader’s pose is defined with respect to the camera coordinate frame. Also included are the camera intrinsics matrix K for each sequence along with a common 3D mesh model for the spreader. The spreader’s mesh model is supplied in PLY format. For a certain image frame, a model vertex M transforms to the camera coordinate system as R*M + t, R and t being the spreader’s pose (R is the equivalent rotation matrix). The homogeneous coordinates of that vertex’s projection on the image frame are K*(R*M + t). The dataset can support research on topics such as object localization, object detection, pose estimation, tracking, etc. If you use this dataset in your research work, you are kindly asked to cite the following paper in your publications: M. Lourakis and M. Pateraki, "Markerless Visual Tracking of a Container Crane Spreader," 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021, pp. 2579-2586, doi: 10.1109/ICCVW54120.2021.00291.

The sequences have been acquired by two different PtGrey color GigE cameras, FL3-GE-28S4C-C and BFLY-PGE-31S4C-C. The frames of each sequence are in a separate RAR archive. The spreader 3D model is in spreader40ft.ply.

Keywords

dataset, object detection, tracking, pose estimation, computer vision, object localization

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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