Title

Real-Time Continuous Action Detection And Recognition Using Depth Images And Inertial Signals

Keywords

Action recognition from continuous data streams; Real-time continuous action detection; Simultaenous utilization of depth images and inertial signals for action recognition

Abstract

This paper presents an approach to detect and recognize actions of interest in real-time from a continuous stream of data that are captured simultaneously from a Kinect depth camera and a wearable inertial sensor. Actions of interest are considered to appear continuously and in a random order among actions of non-interest. Skeleton depth images are first used to separate actions of interest from actions of non-interest based on pause and motion segments. Inertial signals from a wearable inertial sensor are then used to improve the recognition outcome. A dataset consisting of simultaneous depth and inertial data for the smart TV actions of interest occurring continuously and in a random order among actions of non-interest is studied and made publicly available. The results obtained indicate the effectiveness of the developed approach in coping with actions that are performed realistically in a continuous manner.

Publication Date

8-3-2017

Publication Title

IEEE International Symposium on Industrial Electronics

Number of Pages

1342-1347

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ISIE.2017.8001440

Socpus ID

85029902727 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85029902727

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