P
Pichao Wang
Researcher at Alibaba Group
Publications - 105
Citations - 4803
Pichao Wang is an academic researcher from Alibaba Group. The author has contributed to research in topics: Convolutional neural network & Computer science. The author has an hindex of 30, co-authored 89 publications receiving 3388 citations. Previous affiliations of Pichao Wang include Information Technology University & Tianjin University.
Papers
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Proceedings ArticleDOI
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
TL;DR: In this article, a joint trajectory map (JTM) was proposed to encode spatio-temporal information carried in 3D skeleton sequences into multiple 2D images, referred to as Joint Trajectory Maps (jTM), and ConvNets were adopted to exploit the discriminative features for real-time human action recognition.
Journal ArticleDOI
Action Recognition From Depth Maps Using Deep Convolutional Neural Networks
TL;DR: The proposed method maintained its performance on the large dataset, whereas the performance of existing methods decreased with the increased number of actions, and the method achieved 2-9% better results on most of the individual datasets.
Journal ArticleDOI
Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks
TL;DR: This letter presents an effective method to encode the spatiotemporal information of a skeleton sequence into color texture images, referred to as skeleton optical spectra, and employs convolutional neural networks (ConvNets) to learn the discriminative features for action recognition.
Journal ArticleDOI
Joint Distance Maps Based Action Recognition With Convolutional Neural Networks
TL;DR: An effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition.
Journal ArticleDOI
RGB-D-based human motion recognition with deep learning: A survey
TL;DR: A detailed overview of recent advances in RGB-D-based motion recognition is presented in this paper, where the reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth based, skeleton-based and RGB+D based.