title: Wheelchair detection using cascaded decision tree.
abstract: One of the major goals of healthcare systems is to automatically monitor patients of special needs and alarm the caregivers for providing assistant. In this paper, an efficient single-camera multidirectional wheelchair detector based on a cascaded decision tree (CDT) is proposed to detect a wheelchair and its moving direction simultaneously from video frames for a healthcare system. Our approach combines a decision tree structure and boosted-cascade classifiers to construct a new CDT that can perform early confidence decisions in a hierarchical manner to rapidly reject nonwheelchairs and decide the moving directions. We also impose the tracking history to guide detection routes in the CDT to further reduce detection time and increase detection accuracy. The experiments show over 92% detection rate under cluttered scenes.
authors.affiliation: Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan,. nckuos@iis.sinica.edu.tw
authors.forename: Chun-Rong
authors.initials: CR
authors.lastname: Huang
authors.forename: Pau-Choo Julia
authors.initials: PC
authors.lastname: Chung
authors.forename: Kuo-Wei
authors.initials: KW
authors.lastname: Lin
authors.forename: Sheng-Chieh
authors.initials: SC
authors.lastname: Tseng
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods; D014741:Video Recording; D014910:Wheelchairs