Work

Combining Adaptive Hierarchical Depth Motion Maps With Skeletal Joints for Human Action Recognition

Year: 2018

Type: article

Abstract: This paper presents a new framework for human action recognition by fusing human motion with skeletal joints. First, adaptive hierarchical depth motion maps (AH-DMMs) are proposed to capture the shape and motion cues of action sequences. Specifically, AH-DMMs are calculated over adaptive hierarchical windows and Gabor filters are used to encode the texture information of AH-DMMs. Then, spatial distances of skeletal joint positions are computed to characterize the structure information of the human body. Finally, two types of fusion methods including feature-level fusion and decision-level fusion are employed to combine the motion cues and structure information. The experimental results on public benchmark datasets, i.e., MSRAction3D and UTKinect-Action, show the effectiveness of the proposed method.

Source: IEEE Access

DOI: 10.1109/access.2018.2886362

Authors: Runwei Ding, Qinqin He, Hong Liu, Mengyuan Liu

Institutions: Peking University, Nanyang Technological University

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: National Natural Science Foundation of China