A Collaborative Augmented Reality System Based On Real Time Hand Gesture Recognition

Human computer interaction is a major issue in research industry. In order to offer a way to enable untrained users to interact with computer more easily and efficiently gesture based interface has been paid more attention. Gesture based interface provides the most effective means for non-verbal interaction. Various devices like head mounted display and hand glove could be used by the user but they may be cumbersome to use and they limits the user action and make them tired. This problem can be solved by the real time bare hand gesture recognition technique for human computer interaction using computer vision Computer vision is becoming very popular now a days since it can hold a lot of information at a very low cost. With this increasing popularity of computer vision there is a rapid development in the field of virtual reality as it provides an easy and efficient virtual interface between human and computer. At the same time much research is going on to provide more natural interface for human-computer interaction with the power of computer vision .The most powerful and natural interface for human-computer interaction is the hand gesture. In this project we focus our attention to vision based recognition of hand gesture for personal authentication where hand gesture is used as a password. Different hand gestures are used as password for different personals

Object Based Augmented Reality Case Study- Literature Survey on Application based approach towards Augmented Reality

This paper is about Augmented Reality (AR) using object-based visualization and implementation on the smartphone devices. Augmented Reality (AR) employs computer vision, image processing and computer graphics techniques to merge digital content into the real world. It enables real-time interaction between the user, real objects and virtual objects. AR can, for example, be used to embed 2D graphics into a video in such a way as if the virtual elements were part of the real environment. In this work, we are designing AR based software in which we are solving the problem for ease of access of documents on check post. One of the challenges of AR is to align virtual data with the environment. A marker-based approach solves the problem using visual markers, e.g. 2D barcodes, detectable with computer vision methods

A Computer Vision Based Collaborative Augmented Reality Method For Human-Computer Interaction

Computer vision is becoming very popular now a days since it can hold a lot of information at a very low cost. With this increasing popularity of computer vision there is a rapid development in the field of virtual reality as it provides an easy and efficient virtual interface between human and computer. At the same time much research is going on to provide more natural interface for human-computer interaction with the power of computer vision .the most powerful and natural interface for human-computer interaction is the hand gesture. Hand replaces the currently used cumbersome and inefficient devices like mouse and keyboard and with the bare hands one can easily communicate with the computer. This paper explores a system where hand gesture can be effectively used as a password in the login process for authentication of a person using just a simple web camera. Also this technique does not need any special device like head-mounted display, gloves or any special camera that operates beyond visible spectrum. So with this idea, with a simple video camera and bare hands, a person can interact with computer

Generation and Rendering of Interactive Ground Vegetation for Real-Time Testing and Validation of Computer Vision Algorithms

During the development process of new algorithms for computer vision applications, testing and evaluation in real outdoor environments is time-consuming and often difficult to realize. Thus, the use of artificial testing environments is a flexible and cost-efficient alternative. As a result, the development of new techniques for simulating natural, dynamic environments is essential for real-time virtual reality applications, which are commonly known as Virtual Testbeds. Since the first basic usage of Virtual Testbeds several years ago, the image quality of virtual environments has almost reached a level close to photorealism even in real-time due to new rendering approaches and increasing processing power of current graphics hardware. Because of that, Virtual Testbeds can recently be applied in application areas like computer vision, that strongly rely on realistic scene representations. The realistic rendering of natural outdoor scenes has become increasingly important in many application areas, but computer simulated scenes often differ considerably from real-world environments, especially regarding interactive ground vegetation. In this article, we introduce a novel ground vegetation rendering approach, that is capable of generating large scenes with realistic appearance and excellent performance. Our approach features wind animation, as well as object-to-grass interaction and delivers realistically appearing grass and shrubs at all distances and from all viewing angles. This greatly improves immersion, as well as acceptance, especially in virtual training applications. Nevertheless, the rendered results also fulfill important requirements for the computer vision aspect, like plausible geometry representation of the vegetation, as well as its consistence during the entire simulation. Feature detection and matching algorithms are applied to our approach in localization scenarios of mobile robots in natural outdoor environments. We will show how the quality of computer vision algorithms is influenced by highly detailed, dynamic environments, like observed in unstructured, real-world outdoor scenes with wind and object-to-vegetation interaction

A Low Cost Virtual Reality Human Computer Interface for CAD Model Manipulation

Interactions with high volume complex three-dimensional data using traditional two-dimensional computer interfaces have, historically, been inefficient and restrictive. However, during the past decade, virtual reality (VR) has presented a new paradigm for human-computer interaction. This paper presents a VR human-computer interface system, which aims at providing a solution to the human-computer interaction problems present in today’s computer-aided design (CAD) software applications. A data glove device is used as a 3D interface for CAD model manipulation in a virtual design space. To make the visualization more realistic, real-time active stereo vision is provided using LCD shutter glasses. To determine the ease of use and intuitiveness of the interface, a human subject study was conducted for performing standard CAD manipulation tasks. Analysis results and technical issues are also presented and discussed

On human motion prediction using recurrent neural networks

Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state-of-the-art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which result in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.Comment: Accepted at CVPR 1