title: Appearance-based multimodal human tracking and identification for healthcare in the digital home.
abstract: 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.
authors.affiliation: Department of Computer Science & Information Engineering, National Dong-Hwa University, No. 1, Sec. 2, Da-Hsueh Rd., Shoufeng, Hualien 974, Taiwan. mtyang@mail.ndhu.edu.tw.
authors.forename: Mau-Tsuen
authors.initials: MT
authors.lastname: Yang
authors.affiliation: Department of Computer Science & Information Engineering, National Dong-Hwa University, No. 1, Sec. 2, Da-Hsueh Rd., Shoufeng, Hualien 974, Taiwan. 610221034@ems.ndhu.edu.tw.
authors.forename: Shen-Yen
authors.initials: SY
authors.lastname: Huang
mesh_terms: D001185:Artificial Intelligence; D003695:Delivery of Health Care / Q000379:methods*; D005145:Face; D006801:Humans; D007062:Identification, Psychological
references.citation: Sensors (Basel). 2012 Dec 07;12(12):16920-36
references.pmid: 23223150
references.citation: IEEE Trans Pattern Anal Mach Intell. 2012 Jul;34(7):1409-22
references.pmid: 22156098
references.citation: Sensors (Basel). 2012;12(5):6695-711
references.pmid: 22778665
references.citation: J Cogn Neurosci. 1991 Winter;3(1):71-86
references.pmid: 23964806
references.citation: Sensors (Basel). 2013 Dec 10;13(12):16985-7005
references.pmid: 24335727
references.citation: IEEE Trans Pattern Anal Mach Intell. 2005 Oct;27(10):1631-43
references.pmid: 16237997
references.citation: Psychol Sci. 2013 Nov 1;24(11):2235-43
references.pmid: 24068115