View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition(IEEE 2018) 论文地址:https://arxiv.org/pdf/1804.07453.pdf 一种自我调节的视图自适应方案,基于学习的动作过程中自动确定虚拟观察视点数据驱动方式,该方案自适应地重新定位观察视点,以便于更好地从骨架数据中识别动作。 对...
Human action recognition from skeleton data has drawn a lot of attention from researchers due to the availability of thousands of real videos with many challenges. Existing works attempted to model the spatial characteristics and temporal dependencies of 3D joints using dynamic time warping, hand-...
Skeleton-based Human Action Recognition (SHAR) is one of the most trending research topics in computer vision, which relies on the investigation of multi-modal data acquired from different sensory devices. Due to its faster execution speed, skeleton-base
论文翻译:Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks,程序员大本营,技术文章内容聚合第一站。
论文题目:InfoGCN: Representation Learning for Human Skeleton-based Action Recognition(Infogcn:面向人体骨架动作识别的表示学习) 作者&团队:Chi H, Ha M H, Chi S, et al. 1.电工与计算机工程学院,普渡大学,西拉法耶特,美国 2.Kaist,大田,韩国 ...
论文翻译:Skeleton Based Human Action Recognition with Global Context-Aware Attention LSTM Networks 摘要三维骨骼序列中的人体动作识别已经引起了人们的广泛关注。最近,由于长短期记忆(LSTM)网络在序列数据的依赖性和动态性建模方面的优势,在这方面表现出了良好的性能。并不是所有的骨骼关节都具有动作识别的信息性,...
action recognition motivation Both recurrent and convolutional operations are neighborhood-based local operations either in space or time; hence local-range information is repeatedly extracted and propagated to capture long-range dependencies. Many works have designed networks with hierarchical structure to ob...
Skeleton-based Action Recognition 传统的基于深度学习的方法将人体骨骼建模为关节坐标向量序列 [18,28,7,30,35,13] 或伪图像 [14,9,10,11,6],然后将其输入 RNN 或 CNN 以预测动作。然而,将骨架数据表示为向量序列或 2D 网格并不能完全表达相关关节之间的依赖关系,因为人体骨架自然地构造为图形。最近,基于 ...
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition 2018-01-28 15:45:13 研究背景和动机: 行人动作识别(Human Action Recognition)主要从多个模态的角度来进行研究,即:appearance,depth,optical-flow,以及 body skeletons。这其中,动态的人类骨骼点 通常是最具有信息量的,且能够和其他...
行为识别常用来表示特征的形式有:RGB frames , optical flows, audio waves, and human skeletons。其中,human skeleton是由姿态估计器提取的联合坐标列表的序列表示的。 而在基于骨架的动作识别,图神经网络(GCN)的方法最流行,但图神经网络方法有以下局限: ...