The PI's goal in this project is to develop new methods for automatically annotating, recognizing, and indexing large vocabularies of gestures, and to use these methods to create an integrated set of tools for sign language recognition. Current state-of-the-art methods for recognizing large vocabularies of gestures have significant limitations that impact both system design and the user experience. Many methods assume the existence of a near-perfect hand detector/tracker; that is a limiting assumption, which prevents deployment of these methods in complex real-world settings where such accuracy is unachievable. In the absence of perfect hand detectors, system design may involve a large investment in manual annotation of training videos (e.g., specifying hand locations), so as to provide sufficiently clean information to training modules. The user experience is affected by the limited accuracy and robustness of existing applications. In this research the PI will address these issues by explicitly designing recognition and indexing methods that require neither perfect hand detectors nor extensive manual annotations, thus making it substantially easier to deploy accurate and efficient gesture recognition systems in real-world settings. The PI will achieve these objectives through theoretical advances in the current state of the art in computer vision, pattern recognition, and database indexing. The unifying theme in the project is the integration of low-level tracking modules that produce imperfect output, with recognition and indexing methods that are designed to take as input this imperfect output from the tracking modules. Novel articulated tracking methods will be developed that utilize probabilistic graph models to provide fully automatic long-term tracking, while improving upon the excessive time complexity that probabilistic graph models currently incur. New methods will be designed for extracting and exploiting information from hand appearance. As these novel modeling and recognition methods will violate standard assumptions made by existing indexing methods, new indexing methods will be formulated which will improve the efficiency of search in large databases of dynamic gestures and static hand shapes within the proposed framework.
Broader Impacts: Project outcomes will significantly improve the ability of sign language users around the world to search databases of sign language videos and to perform tasks such as looking up the meaning of an unknown sign or retrieving occurrences of a sign of interest in videos of continuous signing. These search tools will have an impact in educational settings, facilitating both learning a sign language and accessing arbitrary information available in a sign language. To these ends, the PI will make his software freely available to the public online. He will also work with experts in American Sign Language to implement key applications using his tools, which will be made available to Deaf students. The PI will furthermore develop a publicly available package of gesture recognition source code, applications, and datasets that will help student researchers at all levels engage in gesture recognition research. As an additional outreach activity intended to attract young people to careers in science, the PI will co-organize summer camps that educate junior high and high school students in computer science.
