FuDensityNet: Fusion-Based Density-Enhanced Network for Occlusion Handling

Zainab Ouardirhi, Zainab Ouardirhi, Otmane Amel, Mostapha Zbakh, Sidi Mahmoudi

2024

Abstract

Our research introduces an innovative approach for detecting occlusion levels and identifying objects with varying degrees of occlusion. We integrate 2D and 3D data through advanced network architectures, utilizing voxelized density-based occlusion assessment for improved visibility of occluded objects. By combining 2D image and 3D point cloud data through carefully designed network components, our method achieves superior detection accuracy in complex scenarios with occlusions. Experimental evaluation demonstrates adaptability across concatenation techniques, resulting in notable Average Precision (AP) improvements. Despite initial testing on a limited dataset, our method shows competitive performance, suggesting potential for further refinement and scalability. This research significantly contributes to advancements in effective occlusion handling for object detection methodologies. The abstract and conclusion highlight the substantial increase in AP achieved through our model.

Paper Citation

in Harvard Style

Ouardirhi Z., Amel O., Zbakh M. and Mahmoudi S. (2024). FuDensityNet: Fusion-Based Density-Enhanced Network for Occlusion Handling. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 632-639. DOI: 10.5220/0012425400003660

in Bibtex Style

@conference{visapp24,
author={Zainab Ouardirhi and Otmane Amel and Mostapha Zbakh and Sidi Mahmoudi},
title={FuDensityNet: Fusion-Based Density-Enhanced Network for Occlusion Handling},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={632-639},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012425400003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - FuDensityNet: Fusion-Based Density-Enhanced Network for Occlusion Handling
SN - 978-989-758-679-8
AU - Ouardirhi Z.
AU - Amel O.
AU - Zbakh M.
AU - Mahmoudi S.
PY - 2024
SP - 632
EP - 639
DO - 10.5220/0012425400003660
PB - SciTePress