Work

Joint Distance Maps Based Action Recognition With Convolutional Neural Networks

Year: 2017

Type: article

Abstract: Motivated by the promising performance achieved by deep learning, 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. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2017.2678539

Authors: Chuankun Li, Yonghong Hou, Pichao Wang, Wanqing Li

Institutions: Tianjin University, University of Wollongong

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Award: 用户体验质量驱动的立体视频可靠传输理论与技术研究