Methods for Human Motion Analysis for American Sign Language Recognition and Assistive Environments

METHODS FOR HUMAN MOTION ANALYSIS FOR AMERICAN SIGN LANGUAGE RECOGNITION AND ASSISTIVE ENVIRONMENTS ZHONG ZHANG, Ph.D. The University of Texas at Arlington, 2015 Supervising Professor: Vassilis Athitsos The broad application domain of the work presented in this thesis is human motion analysis with a focus on hand detection for American Sign Language recognition and fall detection for assistive environments. One of the motivations of the proposed thesis is a semi-automatic vision based American Sign Language recognition system. This system allows a user to submit as query a video of the sign of interest, or simply perform the sign in front of a camera. The system then asks the user to annotate the hands’ locations in the sign. Next, the hand trajectory of the query sign is compared with the models in a large sign database to find the best matches. At last, the user reviews the top results to verify which of them best matches the query sign. Towards making the system more automatic, a novel hand detection method is introduced which is a combination of four representative hand detection methods published in these years. On the topic of fall detection for assistive environments, the work in this thesis aims at improving the safety of patients and elderly persons living unaccompanied at home. More specifically, this thesis proposes a fully automatic vision based fall detec-