A Navigation and Augmented Reality System for Visually Impaired People

In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback.

FSB-DReViSeR: Flow Splitting-Based Dynamic Replacement of Virtual Service Resources for Mobile Users in Virtual Heterogeneous Networks

Virtual networks are sets of virtual devices that are interconnected through a physical network to provide services to end users. These services are usually heterogeneous (VOIP, VoD, streaming, etc.), exploit various amounts of resources (bandwidth, computing power, servers, etc.), and have topologies different from those of the substrate network. These variations in requirements are traditionally known as the architectural flexibility of virtual networks. Each virtual service is provided through a server called a virtual service resource. When a virtual service resource can no longer provide a good quality of service to end users due to the traffic variation generated by their mobility, two approaches are commonly implemented: provisioning the virtual network with resources or replacing the virtual service resource by migrating the service to another node that offers the most suitable amount of resource to satisfy the quality of service (QoS). In this paper, we propose a flow splitting-based dynamic virtual service resource replacement approach that allows for virtual service replacement across multiple virtual paths. Our approach is based on a graph topology that differs from those in the literature, which are based on tree topologies. The simulations performed in this study show that our approach significantly reduces the virtual service resource replacement time compared to other approaches.

Multiple Continuous Virtual Paths Based Cross-View Action Recognition

In this paper, we propose a novel method for cross-view action recognition via multiple continuous virtual paths which connect the source view and the target view. Each point on one virtual path is a virtual view which is obtained by a linear transformation of an action descriptor. All the virtual views are concatenated into an infinite-dimensional feature to characterize continuous changes from the source to the target view. To utilize these infinite-dimensional features directly, we propose a virtual view kernel (VVK) to compute the similarity between two infinite-dimensional features, which can be readily used to construct any kernelized classifiers. In addition, a constraint term is introduced to fully utilize the information contained in the unlabeled samples which are easier to obtain from the target view. The rationality behind the constraint is that any action video belongs to only one class. To further explore complementary visual information, we utilize multiple continuous virtual paths. The original source and target views are projected to different auxiliary source and target views using the random projection technique. Then we fuse all the VVKs generated from all pairs of auxiliary views. Our method is verified on the IXMAS and MuHAVi datasets, and the experimental results demonstrate that our method achieves better performance than the state-of-the-art methods.

Application of Vega Path Tool in Visual Simulation System for Bridge Cranes

Aiming at the path sudden change and inconsistent turning of virtual vehicles in visual simulation system for bridge cranes, forklifts virtual paths are created by using path and navigator modules in Lynx interface based on the application principles of path and navigator in Vega software. Then, forklifts visual paths are edited to ensure the natural and fluent movements of forklifts by choosing spline navigator, setting smaller smoothing threshold and smaller spline tension parameters in Path Tool. Finally, combining working cycle requirements of loading and unloading goods for bridge cranes, continuous motions of several forklifts are realized by calling the path navigation library functions of Vega Software in VC++. Therefore, virtual vehicles complex paths are created and disadvantages of fixed path for virtual vehicles in visual simulation system are solved.