Real-time 2D Static Hand Gesture Recognition and 2D Hand Tracking for Human-Computer Interaction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Real-time 2D Static Hand Gesture Recognition and 2D Hand Tracking for Human-Computer Interaction write by Pavel Alexandrovich Popov. This book was released on 2020. Real-time 2D Static Hand Gesture Recognition and 2D Hand Tracking for Human-Computer Interaction available in PDF, EPUB and Kindle. The topic of this thesis is Hand Gesture Recognition and Hand Tracking for user interface applications. 3 systems were produced, as well as datasets for recognition and tracking, along with UI applications to prove the concept of the technology. These represent significant contributions to resolving the hand recognition and tracking problems for 2d systems. The systems were designed to work in video only contexts, be computationally light, provide recognition and tracking of the user's hand, and operate without user driven fine tuning and calibration. Existing systems require user calibration, use depth sensors and do not work in video only contexts, or are computationally heavy requiring GPU to run in live situations. A 2-step static hand gesture recognition system was created which can recognize 3 different gestures in real-time. A detection step detects hand gestures using machine learning models. A validation step rejects false positives. The gesture recognition system was combined with hand tracking. It recognizes and then tracks a user's hand in video in an unconstrained setting. The tracking uses 2 collaborative strategies. A contour tracking strategy guides a minimization based template tracking strategy and makes it real-time, robust, and recoverable, while the template tracking provides stable input for UI applications. Lastly, an improved static gesture recognition system addresses the drawbacks due to stratified colour sampling of the detection boxes in the detection step. It uses the entire presented colour range and clusters it into constituent colour modes which are then used for segmentation, which improves the overall gesture recognition rates. One dataset was produced for static hand gesture recognition which allowed for the comparison of multiple different machine learning strategies, including deep learning. Another dataset was produced for hand tracking which provides a challenging series of user scenarios to test the gesture recognition and hand tracking system. Both datasets are significantly larger than other available datasets. The hand tracking algorithm was used to create a mouse cursor control application, a paint application for Android mobile devices, and a FPS video game controller. The latter in particular demonstrates how the collaborating hand tracking can fulfill the demanding nature of responsive aiming and movement controls.
Real-time Hand Gesture Detection and Recognition for Human Computer Interaction
Real-time Hand Gesture Detection and Recognition for Human Computer Interaction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Real-time Hand Gesture Detection and Recognition for Human Computer Interaction write by Nasser Hasan Abdel-Qader Dardas. This book was released on 2012. Real-time Hand Gesture Detection and Recognition for Human Computer Interaction available in PDF, EPUB and Kindle. This thesis focuses on bare hand gesture recognition by proposing a new architecture to solve the problem of real-time vision-based hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bare hand in a cluttered background using face subtraction, skin detection and contour comparison. The second stage allows recognizing hand gestures using bag-of-features and multi-class Support Vector Machine (SVM) algorithms. Finally, a grammar has been developed to generate gesture commands for application control. Our hand gesture recognition system consists of two steps: offline training and online testing. In the training stage, after extracting the keypoints for every training image using the Scale Invariance Feature Transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multi-class SVM to build the classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using my algorithm. Then, the keypoints are extracted for every small image that contains the detected hand posture and fed into the cluster model to map them into a bag-of-words vector, which is fed into the multi-class SVM classifier to recognize the hand gesture. Another hand gesture recognition system was proposed using Principle Components Analysis (PCA). The most eigenvectors and weights of training images are determined. In the testing stage, the hand posture is detected for every frame using my algorithm. Then, the small image that contains the detected hand is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined among the test weights and the training weights of each training image to recognize the hand gesture. Two application of gesture-based interaction with a 3D gaming virtual environment were implemented. The exertion videogame makes use of a stationary bicycle as one of the main inputs for game playing. The user can control and direct left-right movement and shooting actions in the game by a set of hand gesture commands, while in the second game, the user can control and direct a helicopter over the city by a set of hand gesture commands.
Color and Illumination Independent Hand Tracking and Gesture Recognition
Color and Illumination Independent Hand Tracking and Gesture Recognition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Color and Illumination Independent Hand Tracking and Gesture Recognition write by . This book was released on 2006. Color and Illumination Independent Hand Tracking and Gesture Recognition available in PDF, EPUB and Kindle. Recognition of human motion provides hints to understand human activities and gives opportunities to the development of a new human-computer interaction (HCI) interface. Hidden Markov Models (HMMs) are used for visual recognition of complex, structured hand gestures such as the ones found in a sign language, since they have proved their success in recognizing speech and handwriting. In this paper, we introduce a hand gesture recognition system to recognize gestures in real-time. Hand tracking is performed in two different ways. The first method is based on color segmentation and blob generation over the hand region, and the second method uses block matching and particle filtering algorithms to detect the moving hand which makes the system totally color and illumination independent. In both methods, extracted information is used as the input to the HMM based gesture recognizer.
Real-time Immersive Human-computer Interaction Based on Tracking and Recognition of Dynamic Hand Gestures
Real-time Immersive Human-computer Interaction Based on Tracking and Recognition of Dynamic Hand Gestures - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Real-time Immersive Human-computer Interaction Based on Tracking and Recognition of Dynamic Hand Gestures write by Gan Lu. This book was released on 2011. Real-time Immersive Human-computer Interaction Based on Tracking and Recognition of Dynamic Hand Gestures available in PDF, EPUB and Kindle. With fast developing and ever growing use of computer based technologies, human-computer interaction (HCI) plays an increasingly pivotal role. In virtual reality (VR), HCI technologies provide not only a better understanding of three-dimensional shapes and spaces, but also sensory immersion and physical interaction. With the hand based HCI being a key HCI modality for object manipulation and gesture based communication, challenges are presented to provide users a natural, intuitive, effortless, precise, and real-time method for HCI based on dynamic hand gestures, due to the complexity of hand postures formed by multiple joints with high degrees-of-freedom, the speed of hand movements with highly variable trajectories and rapid direction changes, and the precision required for interaction between hands and objects in the virtual world. Presented in this thesis is the design and development of a novel real-time HCI system based on a unique combination of a pair of data gloves based on fibre-optic curvature sensors to acquire finger joint angles, a hybrid tracking system based on inertia and ultrasound to capture hand position and orientation, and a stereoscopic display system to provide an immersive visual feedback. The potential and effectiveness of the proposed system is demonstrated through a number of applications, namely, hand gesture based virtual object manipulation and visualisation, hand gesture based direct sign writing, and hand gesture based finger spelling. For virtual object manipulation and visualisation, the system is shown to allow a user to select, translate, rotate, scale, release and visualise virtual objects (presented using graphics and volume data) in three-dimensional space using natural hand gestures in real-time. For direct sign writing, the system is shown to be able to display immediately the corresponding SignWriting symbols signed by a user using three different signing sequences and a range of complex hand gestures, which consist of various combinations of hand postures (with each finger open, half-bent, closed, adduction and abduction), eight hand orientations in horizontal/vertical plans, three palm facing directions, and various hand movements (which can have eight directions in horizontal/vertical plans, and can be repetitive, straight/curve, clockwise/anti-clockwise). The development includes a special visual interface to give not only a stereoscopic view of hand gestures and movements, but also a structured visual feedback for each stage of the signing sequence. An excellent basis is therefore formed to develop a full HCI based on all human gestures by integrating the proposed system with facial expression and body posture recognition methods. Furthermore, for finger spelling, the system is shown to be able to recognise five vowels signed by two hands using the British Sign Language in real-time.
Real-Time Vision for Human-Computer Interaction
Real-Time Vision for Human-Computer Interaction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Real-Time Vision for Human-Computer Interaction write by Branislav Kisacanin. This book was released on 2005-08-23. Real-Time Vision for Human-Computer Interaction available in PDF, EPUB and Kindle. The need for natural and effective Human-Computer Interaction (HCI) is increasingly important due to the prevalence of computers in human activities. Computer vision and pattern recognition continue to play a dominant role in the HCI realm. However, computer vision methods often fail to become pervasive in the field due to the lack of real-time, robust algorithms, and novel and convincing applications. This state-of-the-art contributed volume is comprised of articles by prominent experts in computer vision, pattern recognition and HCI. It is the first published text to capture the latest research in this rapidly advancing field with exclusive focus on real-time algorithms and practical applications in diverse and numerous industries, and it outlines further challenges in these areas. Real-Time Vision for Human-Computer Interaction is an invaluable reference for HCI researchers in both academia and industry, and a useful supplement for advanced-level courses in HCI and Computer Vision.
