Multi-Temporal Depth Motion Maps-Based Local Binary Patterns for 3D Human Action Recognition

Chen, Chen and Liu, Mengyang and Liu, Hong and Zhang, Baochang and Han, Jungong and Kahtarnavaz, Nasser (2017) Multi-Temporal Depth Motion Maps-Based Local Binary Patterns for 3D Human Action Recognition. IEEE Access, 5. pp. 22590-22604. ISSN 2169-3536

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Abstract

This paper presents a local spatio-temporal descriptor for action recognition from depth video sequences which is capable of distinguishing similar actions as well as coping with different speeds of actions. This descriptor is based on three processing stages. In the first stage, the shape and motion cues are captured from a weighted depth sequence by temporally overlapped depth segments, leading to three improved depth motion maps (DMMs) compared to previously introduced DMMs. In the second stage, the improved DMMs are partitioned into dense patches, from which the local binary patterns histogram features are extracted to characterize local rotation invariant texture information. In the final stage, a Fisher kernel is used for generating a compact feature representation, which is then combined with a kernel-based extreme learning machine (ELM) classifier. The developed solution is applied to five public domain datasets and is extensively evaluated. The results obtained demonstrate the effectiveness of this solution as compared to the existing approaches.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Access
Additional Information:
©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2500
Subjects:
ID Code:
88011
Deposited By:
Deposited On:
06 Oct 2017 19:38
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Apr 2020 05:41