Next Article in Journal
Dual-Element Transducer with Phase-Inversion for Wide Depth of Field in High-Frequency Ultrasound Imaging
Next Article in Special Issue
A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG
Previous Article in Journal
Development of a Modularized Seating System to Actively Manage Interface Pressure
Previous Article in Special Issue
Ambient Agents: Embedded Agents for Remote Control and Monitoring Using the PANGEA Platform
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(8), 14253-14277;

Appearance-Based Multimodal Human Tracking and Identification for Healthcare in the Digital Home

Department of Computer Science & Information Engineering, National Dong-Hwa University, No. 1, Sec. 2, Da-Hsueh Rd., Shoufeng, Hualien 974, Taiwan
Author to whom correspondence should be addressed.
Received: 2 April 2014 / Revised: 3 July 2014 / Accepted: 8 July 2014 / Published: 5 August 2014
(This article belongs to the Special Issue Sensors Data Fusion for Healthcare)
Full-Text   |   PDF [1725 KB, uploaded 5 August 2014]   |  


There is an urgent need for intelligent home surveillance systems to provide home security, monitor health conditions, and detect emergencies of family members. One of the fundamental problems to realize the power of these intelligent services is how to detect, track, and identify people at home. Compared to RFID tags that need to be worn all the time, vision-based sensors provide a natural and nonintrusive solution. Observing that body appearance and body build, as well as face, provide valuable cues for human identification, we model and record multi-view faces, full-body colors and shapes of family members in an appearance database by using two Kinects located at a home’s entrance. Then the Kinects and another set of color cameras installed in other parts of the house are used to detect, track, and identify people by matching the captured color images with the registered templates in the appearance database. People are detected and tracked by multisensor fusion (Kinects and color cameras) using a Kalman filter that can handle duplicate or partial measurements. People are identified by multimodal fusion (face, body appearance, and silhouette) using a track-based majority voting. Moreover, the appearance-based human detection, tracking, and identification modules can cooperate seamlessly and benefit from each other. Experimental results show the effectiveness of the human tracking across multiple sensors and human identification considering the information of multi-view faces, full-body clothes, and silhouettes. The proposed home surveillance system can be applied to domestic applications in digital home security and intelligent healthcare. View Full-Text
Keywords: home healthcare; human detection; human tracking; human identification home healthcare; human detection; human tracking; human identification

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Yang, M.-T.; Huang, S.-Y. Appearance-Based Multimodal Human Tracking and Identification for Healthcare in the Digital Home. Sensors 2014, 14, 14253-14277.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top