3D Pose Nowcasting: Forecast the future to improve the present

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Alessandro Simoni , Francesco Marchetti , Guido Borghi , Federico Becattini , Lorenzo Seidenari , Roberto Vezzani , Alberto Del Bimbo
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引用次数: 0

Abstract

Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.
3D姿态临近投射:预测未来以改善现在
在过去的几年中,使人类和机器人之间安全有效的协作和共存的技术变得非常重要。实现这种协作范例的一个关键组件是使用非侵入性系统理解人类和机器人的3D姿势。因此,在本文中,我们提出了一种新的基于视觉的系统,利用深度数据来准确地建立骨骼关节的三维位置。具体来说,我们引入了姿态临近投射的概念,表明系统能够通过联合学习预测未来姿态来提高当前姿态估计的精度。在两个不同的数据集上进行了实验评估,提供了准确和实时的性能,并验证了所提出方法在机器人和人类场景下的有效性。
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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