No abstract
Gestures are a fundamental part of human communication and are becoming a key component of human-computer interaction. Traditionally, to teach computers to recognize specific gestures, researchers have used a sensor, usually a camera, to collect large gesture datasets, which are then classified and structured using machine learning techniques. Yet finding a way to confidently differentiate between several gesture classes has proven to be rather difficult for those working in the gesture recognition field. To capture the samples of movements necessary to train gesture recognition systems, the first step is to provide research participants with appropriate instructions. As collecting gesture data is the crucial first step of creating a robust gesture dataset, this dissertation will examine the modalities of instruction used in gesture recognition research to examine whether appropriate directives are conveyed to research participants. These experiments will result in the creation of a new dataset, the PJVA-20 dataset, comprised of 50 samples of 20 gesture classes sampled from 6 participants. After collecting the gesture samples of the PJVA-20 dataset, this dissertation will establish the benchmark recognition system PJVA — chiefly comprised of AMFE, Polynomial Motion Approximation, and Principal Component Analysis (PCA)—to contribute to the gesture recognition literature in terms of novel gesture recognition algorithms that can achieve high speed and accuracy results. This also involves examining studies in the gesture recognition literature to determine which machine learning algorithms offer reliability, speed, and accuracy for solving complex gesture recognition problems, as well as experimenting and testing the PJVA approach against other researchers in the Computer Vision and Machine Learning fields. In particular, the MSRC-12 research provides a benchmark point of comparison for research in this field. To test the quality of samples on the PJVA-20 against the MSRC-12, a new method is established for extracting motion feature vectors through a novel gesture recognition approach, AMFE. This is tested by applying PJVA to extract and label gesture data from both the MSRC- 12 and PJVA-20 datasets.
Gestures are a fundamental part of human communication and are becoming a key component of human-computer interaction. Traditionally, to teach computers to recognize specific gestures, researchers have used a sensor, usually a camera, to collect large gesture datasets, which are then classified and structured using machine learning techniques. Yet finding a way to confidently differentiate between several gesture classes has proven to be rather difficult for those working in the gesture recognition field. To capture the samples of movements necessary to train gesture recognition systems, the first step is to provide research participants with appropriate instructions. As collecting gesture data is the crucial first step of creating a robust gesture dataset, this dissertation will examine the modalities of instruction used in gesture recognition research to examine whether appropriate directives are conveyed to research participants. These experiments will result in the creation of a new dataset, the PJVA-20 dataset, comprised of 50 samples of 20 gesture classes sampled from 6 participants. After collecting the gesture samples of the PJVA-20 dataset, this dissertation will establish the benchmark recognition system PJVA — chiefly comprised of AMFE, Polynomial Motion Approximation, and Principal Component Analysis (PCA)—to contribute to the gesture recognition literature in terms of novel gesture recognition algorithms that can achieve high speed and accuracy results. This also involves examining studies in the gesture recognition literature to determine which machine learning algorithms offer reliability, speed, and accuracy for solving complex gesture recognition problems, as well as experimenting and testing the PJVA approach against other researchers in the Computer Vision and Machine Learning fields. In particular, the MSRC-12 research provides a benchmark point of comparison for research in this field. To test the quality of samples on the PJVA-20 against the MSRC-12, a new method is established for extracting motion feature vectors through a novel gesture recognition approach, AMFE. This is tested by applying PJVA to extract and label gesture data from both the MSRC- 12 and PJVA-20 datasets.
This work describes the implementation of various human-robot interaction systems in a functioning mobile robot. This project is the result of integrating a tracking system for human faces and objects, face recognition, gesture recognition, body tracking, stereo-vision, speech synthesis, and voice recognition. The majority of these systems are custom designed for this particular project, these systems and how they were designed are explained in detail throughout this report. A unique vector-based approach is used for gesture recognition. There is minimal focus on the mechanics and electronics of the human-robot interaction system, but rather on the information processing of the robot. Using combinations of many information processing systems will allow robots to interact with human users more naturally, and will provide a natural conduit for future cooperative human-robot efforts. This project lays the groundwork for what will be a large collaborative effort aimed at creating possibly one of the most advanced human interactive robot in the world.
No abstract
This work describes the implementation of various human-robot interaction systems in a functioning mobile robot. This project is the result of integrating a tracking system for human faces and objects, face recognition, gesture recognition, body tracking, stereo-vision, speech synthesis, and voice recognition. The majority of these systems are custom designed for this particular project, these systems and how they were designed are explained in detail throughout this report. A unique vector-based approach is used for gesture recognition. There is minimal focus on the mechanics and electronics of the human-robot interaction system, but rather on the information processing of the robot. Using combinations of many information processing systems will allow robots to interact with human users more naturally, and will provide a natural conduit for future cooperative human-robot efforts. This project lays the groundwork for what will be a large collaborative effort aimed at creating possibly one of the most advanced human interactive robot in the world.
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