Temporal pyramid Matching of local binary sub-patterns for hand-gesture recognition

Human–computer Interaction systems based on hand-gesture recognition are nowadays of great interest to establish a natural communication between humans and machines. However, the visual recognition of gestures and other human poses remains a challenging problem. In this paper, the original volumetric spatiograms of local binary patterns descriptor has been extended to efficiently and robustly encode the spatial and temporal information of hand gestures. This enhancement mitigates the dimensionality problems of the previous approach, and considers more temporal information to achieve a higher recognition rate. Excellent results have been obtained, outperforming other existing approaches of the state of the art

Content-prioritised video coding for British Sign Language communication.

Video communication of British Sign Language (BSL) is important for remote interpersonal communication and for the equal provision of services for deaf people. However, the use of video telephony and video conferencing applications for BSL communication is limited by inadequate video quality. BSL is a highly structured, linguistically complete, natural language system that expresses vocabulary and grammar visually and spatially using a complex combination of facial expressions (such as eyebrow movements, eye blinks and mouth/lip shapes), hand gestures, body movements and finger-spelling that change in space and time. Accurate natural BSL communication places specific demands on visual media applications which must compress video image data for efficient transmission. Current video compression schemes apply methods to reduce statistical redundancy and perceptual irrelevance in video image data based on a general model of Human Visual System (HVS) sensitivities. This thesis presents novel video image coding methods developed to achieve the conflicting requirements for high image quality and efficient coding. Novel methods of prioritising visually important video image content for optimised video coding are developed to exploit the HVS spatial and temporal response mechanisms of BSL users (determined by Eye Movement Tracking) and the characteristics of BSL video image content. The methods implement an accurate model of HVS foveation, applied in the spatial and temporal domains, at the pre-processing stage of a current standard-based system (H.264). Comparison of the performance of the developed and standard coding systems, using methods of video quality evaluation developed for this thesis, demonstrates improved perceived quality at low bit rates. BSL users, broadcasters and service providers benefit from the perception of high quality video over a range of available transmission bandwidths. The research community benefits from a new approach to video coding optimisation and better understanding of the communication needs of deaf people

Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

A more natural, intuitive, user-friendly, and less intrusive Human–Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art

Two Hand Gesture Based 3D Navigation in Virtual Environments

Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE. The proposed technique also allow users to efficiently control speed during navigation. The proposed technique is implemented via a VE for experimental purposes. Forty (40) participants performed the experimental study. Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users. Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures. kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%). kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec

An integrated sign language recognition system

Doctor EducationisResearch has shown that five parameters are required to recognize any sign language gesture: hand shape, location, orientation and motion, as well as facial expressions. The South African Sign Language (SASL) research group at the University of the Western Cape has created systems to recognize Sign Language gestures using single parameters. Using a single parameter can cause ambiguities in the recognition of signs that are similarly signed resulting in a restriction of the possible vocabulary size. This research pioneers work at the group towards combining multiple parameters to achieve a larger recognition vocabulary set. The proposed methodology combines hand location and hand shape recognition into one combined recognition system. The system is shown to be able to recognize a very large vocabulary of 50 signs at a high average accuracy of 74.1%. This vocabulary size is much larger than existing SASL recognition systems, and achieves a higher accuracy than these systems in spite of the large vocabulary. It is also shown that the system is highly robust to variations in test subjects such as skin colour, gender and body dimension. Furthermore, the group pioneers research towards continuously recognizing signs from a video stream, whereas existing systems recognized a single sign at a time. To this end, a highly accurate continuous gesture segmentation strategy is proposed and shown to be able to accurately recognize sentences consisting of five isolated SASL gestures

Basic Relational Concept and Verbal Behavior Development in Preschool Children With and Without Autism Spectrum Disorder

The current study investigates basic, relational concept development, as measured by the Boehm Test of Basic Concepts 3rd Edition – Preschool Version (BTBC3-P), in 51 preschool aged children (Mage = 49.26 months; SD = 8.53 months) with and without Autism Spectrum Disorder (ASD) attending the same Comprehensive Application of Behavior Analysis to Schooling (CABAS©) preschool. Relational concepts represent spatial, dimensional, temporal, quantitative, and class relationships between objects or people (i.e., above and behind). They predict academic achievement in grades two and three and are essential for following directions, making comparisons, sequencing, and classifying—the foundational skills for more complex problem solving (Boehm, 2013; Steinbauer & Heller, 1978). Relational concepts are difficult to learn, represent less tangible and stable relationships, and are often acquired incidentally (Boehm, 2001). Research in Applied Behavior Analysis (ABA) has found that incidental learning generally does not occur until a child masters the naming capability (Greer & Longano, 2010). Naming is a phenomenon that involves a circular understanding whereby a child can see a nonverbal term (i.e., a picture or a word), name that term, hear themselves naming the term, and then select the appropriate representation of that term without direct instruction (Horne & Lowe 1996). Naming is the mechanism through which success in traditional classroom settings is possible, such that once a child has attained the naming capability, that child can learn through observation or by asking questions if he/she sees or hears something novel (i.e., “What is pesto?” Greer & Longano, 2010; Greer & Speckman, 2009). Considering the widespread use of ABA to help children with ASD develop language, this study investigated relational concept acquisition using an ABA (i.e., Verbal Behavior Development Theory [VBDT]) framework. Overall, preschool children with ASD knew significantly fewer total concepts, quantitative concepts, and spatial concepts than their typically developing (TD) counterparts. In addition, the more VBD cusps and capabilities a child attained, the more concepts he/she correctly identified (R2 VBD= .054 with diagnosis held constant). Further, regardless of diagnosis and student progression of VBD, naming was a significant predictor of total concepts known (R2 naming = .114), as well as of concepts known not covered in the C-PIRK© curriculum (R2 naming = .099) used at the preschool. The latter finding supports previous studies that identify naming as a prerequisite to incidental learning. A secondary aim of this dissertation investigated the actions of the examiner required to keep children motivated and on task by creating an Assessor’s Tactic Checklist that lists a number of behavioral techniques to build motivation and increase assessment validity. Overall, diagnosis and naming were related to the number of assessor’s tactics used, with those children with ASD and children without naming requiring significantly more types of tactics than those without (approximately two more types for ASD and two and a half more types for those without naming). Implications for future studies include exploring the rate of concept learning pre and post naming acquisition as well as working to uncover the mechanisms through which naming affects concept acquisition. There is also an identified need for continued exploration into the usefulness of an Assessor’s Tactic Checklist. Strengths and weaknesses of the study are also addressed

Review and research into the motor systems of Kephart and Getman

The purpose of this paper was to discuss the theories of Kephart and Getman, and to review the research done in the late 1960s and early 1970s on the effectiveness of perceptual-motor and sensory-motor programs designed on their theories and developmental training programs

Deep Learning-Based Action Recognition

The classification of human action or behavior patterns is very important for analyzing situations in the field and maintaining social safety. This book focuses on recent research findings on recognizing human action patterns. Technology for the recognition of human action pattern includes the processing technology of human behavior data for learning, technology of expressing feature values ​​of images, technology of extracting spatiotemporal information of images, technology of recognizing human posture, and technology of gesture recognition. Research on these technologies has recently been conducted using general deep learning network modeling of artificial intelligence technology, and excellent research results have been included in this edition