Designing 2D Interfaces For 3D Gesture Retrieval Utilizing Deep Learning

Gesture retrieval can be defined as the process of retrieving the correct meaning of the hand movement from a pre-assembled gesture dataset. The purpose of the research discussed here is to design and implement a gesture interface system that facilitates retrieval for an American Sign Language gesture set using a mobile device. The principal challenge discussed here will be the normalization of 2D gestures generated from the mobile device interface and the 3D gestures captured from video samples into a common data structure that can be utilized by deep learning networks. This thesis covers convolutional neural networks and auto encoders which are used to transform 2D gestures into the correct form, before being classified by a convolutional neural network. The architecture and implementation of the front-end and back-end systems and each of their respective responsibilities are discussed. Lastly, this thesis covers the results of the experiment and breakdown the final classification accuracy of 83% and how this work could be further improved by using depth based videos for the 3D data

Towards Automated Large Vocabulary Gesture Search

This paper describes work towards designing a computer vision system for helping users look up the meaning of a sign. Sign lookup is treated as a video database retrieval problem. A video database is utilized that contains one or more video examples for each sign, for a large number of signs (close to 1000 in our current experiments). The emphasis of this paper is on evaluating the tradeoffs between a non-automated approach, where the user manually specifies hand locations in the input video, and a fully automated approach, where hand locations are determined using a computer vision module, thus introducing inaccuracies into the sign retrieval process. We experimentally evaluate both approaches and present their respective advantages and disadvantages