Mobile, collaborative augmented reality using cloudlets

The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment

Leveraging cloudlets for immersive collaborative applications

To enable immersive applications on mobile devices, the authors propose a component-based cyber foraging framework that optimizes application-specific metrics by not only offloading but also configuring application components at runtime. It also enables collaborative scenarios by sharing components between multiple devices

On the Feasibility of Real-Time 3D Hand Tracking using Edge GPGPU Acceleration

This paper presents the case study of a non-intrusive porting of a monolithic C++ library for real-time 3D hand tracking, to the domain of edge-based computation. Towards a proof of concept, the case study considers a pair of workstations, a computationally powerful and a computationally weak one. By wrapping the C++ library in Java container and by capitalizing on a Java-based offloading infrastructure that supports both CPU and GPGPU computations, we are able to establish automatically the required server-client workflow that best addresses the resource allocation problem in the effort to execute from the weak workstation. As a result, the weak workstation can perform well at the task, despite lacking the sufficient hardware to do the required computations locally. This is achieved by offloading computations which rely on GPGPU, to the powerful workstation, across the network that connects them. We show the edge-based computation challenges associated with the information flow of the ported algorithm, demonstrate how we cope with them, and identify what needs to be improved for achieving even better performance.Comment: 6 pages, 5 figure

Performance Analysis of Tracking on Mobile Devices using Local Binary Descriptors

With the growing ubiquity of mobile devices, users are turning to their smartphones and tablets to perform more complex tasks than ever before. Performing computer vision tasks on mobile devices must be done despite the constraints on CPU performance, memory, and power consumption. One such task for mobile devices involves object tracking, an important area of computer vision. The computational complexity of tracking algorithms makes them ideal candidates for optimization on mobile platforms. This thesis presents a mobile implementation for real time object tracking. Currently few tracking approaches take into consideration the resource constraints on mobile devices. Optimizing performance for mobile devices can result in better and more efficient tracking approaches for mobile applications such as augmented reality. These performance benefits aim to increase the frame rate at which an object is tracked and reduce power consumption during tracking. For this thesis, we utilize binary descriptors, such as Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and Rotated BRIEF (ORB), Binary Robust Invariant Scalable Keypoints (BRISK), and Fast Retina Keypoint (FREAK). The tracking performance of these descriptors is benchmarked on mobile devices. We consider an object tracking approach based on a dictionary of templates that involves generating keypoints of a detected object and candidate regions in subsequent frames. Descriptor matching, between candidate regions in a new frame and a dictionary of templates, identifies the location of the tracked object. These comparisons are often computationally intensive and require a great deal of memory and processing time. Google\u27s Android operating system is used to implement the tracking application on a Samsung Galaxy series phone and tablet. Control of the Android camera is largely done through OpenCV\u27s Android SDK. Power consumption is measured using the PowerTutor Android application. Other performance characteristics, such as processing time, are gathered using the Dalvik Debug Monitor Server (DDMS) tool included in the Android SDK. These metrics are used to evaluate the tracker\u27s performance on mobile devices