activity
20192022
most citedKaliCalib: A Framework for Basketball Court Registration

9 citations · 10 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV2024

3D-COCO: extension of MS-COCO dataset for image detection and 3D reconstruction modules

Maxence Bideaux, Alice Phe, Mohamed Chaouch +2

We introduce 3D-COCO, an extension of the original MS-COCO dataset providing 3D models and 2D-3D alignment annotations. 3D-COCO was designed to achieve computer vision tasks such a…

cs.CV20229 cited

KaliCalib: A Framework for Basketball Court Registration

Adrien Maglo, Astrid Orcesi, Quoc Cuong Pham

Tracking the players and the ball in team sports is key to analyse the performance or to enhance the game watching experience with augmented reality. When the only sources for this…

cs.CV2022

Efficient tracking of team sport players with few game-specific annotations

Adrien Maglo, Astrid Orcesi, Quoc-Cuong Pham

One of the requirements for team sports analysis is to track and recognize players. Many tracking and reidentification methods have been proposed in the context of video surveillan…

cs.CV2021

UCP-Net: Unstructured Contour Points for Instance Segmentation

Camille Dupont, Yanis Ouakrim, Quoc Cuong Pham

The goal of interactive segmentation is to assist users in producing segmentation masks as fast and as accurately as possible. Interactions have to be simple and intuitive and the…

cs.CV20211 cited

PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation

Abdallah Benzine, Florian Chabot, Bertrand Luvison +2

Recently, several deep learning models have been proposed for 3D human pose estimation. Nevertheless, most of these approaches only focus on the single-person case or estimate 3D p…

cs.CV2019

Single-shot 3D multi-person pose estimation in complex images

Abdallah Benzine, Bertrand Luvison, Quoc Cuong Pham +1

In this paper, we propose a new single shot method for multi-person 3D human pose estimation in complex images. The model jointly learns to locate the human joints in the image, to…