Augmented reality is a visualization technology that displays information by adding virtual images to the real world. Effective implementation of augmented reality requires recognition of the current scene. Identifying objects in real-time video on computationally limited hardware requires significant effort. One way to solve this problem is to create a hybrid system that, based on machine learning and computer vision technology, processes and analyzes visual data to identify and classify real-world objects. The proposed architecture is based on a combination of the Vuforia augmented system, which provides good performance by balancing prediction accuracy and efficiency. First, the Vuforia neural network architecture allows convenient interaction with AR in Unity and provides initial conditions for detecting 3D objects. The augmented reality construction algorithm is based on the ARCore framework and the OpenGL interface for embedded systems. The system integrates recognition data with an AR platform to display corresponding 3D models, allowing users to interact with them through the functionality of the AR application. This method also involves the development of an enhanced user interface for AR, making the augmented environment more accessible for navigation and control. Experimental research has shown that the proposed method significantly improves the accuracy of object recognition and the ease of working with 3D models in AR.
Currently, there are many options for controlling robotic devices. Human-machine interaction is a key component of the control infrastructure. The most common solution is mobile devices or embedded touch screens, as well as next generation virtual reality devices. In human-machine interaction, most input devices are controlled manually, which is not always convenient, and sometimes even impossible. One option is gesture control, which has become increasingly common in the last few years. This artificial cognitive “sensory perception” or ability is a communication channel between a human and a machine. This article presents a two-steps approach to real-time control robotic devices. The first step is the hand recognition method base on palm detection (SSD Detector) and hand landmark models. After a palm detection, the hand landmark model performs fine localization of the key points of the 3-D coordinate of the hand inside the detected areas of the hand through regression and direct coordinate prediction. The model learns a consistent internal representation of the hand posture and is resistant to even partially visible hands and self-occlusions. The second human gesture recognition step is based on obtaining the coordinates of the hand, the distance from the camera to the hand in space, raised, lowered fingers and other indicators that allow you to accurately determine the shown gesture. In terms of gesture recognition accuracy, the proposed real-time system is better than the state-of-the-art methods.
This paper presents a new method for video segmentation using deep learning neural networks in the quaternion space into sets of objects, background, static and dynamic textures. We introduce a novel quaternionic anisotropic gradient (QAG) which can combine the color channels and the orientations in the image plane. The local polynomial estimates and the ICI rule are used for QAG calculation. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. Using the QAGs, we extract the local orientation information in the color images. Second, to improve the segmentation result we applied neural network to this derived orientation information. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. Experimental comparisons to state-of-the-art video segmentation methods demonstrate the effectiveness of the proposed approach.
The article discusses the problem of constructing an algorithm for the automated detection of operator fatigue and monitoring its state by analyzing data obtained by machine vision systems in the visible range. As information parameters, a combined model is used, which includes an analysis of the speed of movement of the pupils and the degree of scattering of motion eyes. A multi-criteria smoothing method is used to identify the trend curve. The deviation of the scatter of displacements of the focus of view relative to the center of the object also indicates the degree of operator involvement in the technological or controlled process. The speed of movement of the pupils and the spread in the displacements of the focus of view relative to the center of the main large object were recorded. The work contains tables and graphs fixing the result of detecting deviations relative to the values obtained in the first minutes of the operator's work, from the time of tracking the test video.
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