The Road to the blend of Augmented Reality and Intellectual Capital : a Case of Data Management for Outdoor Mobile Augmented Reality

Augmented reality (AR) presents a particularly powerful user interface (UI) to context-aware computing environments in a Knowledge-based economy. AR systems integrate virtual information/intellectual capital into a person's physical environment so that he or she will perceive that information as existing in their surroundings. In a limited mobile platform we propose a framework which covers the main problems of limited resources in mobile, server dependency for data management, processing and network latency, for outdoor mobile augmented reality. This model will be a gateway to explore and apply augmented reality and intellectual capital in future with full spiri

Object Tracking Using Local Binary Descriptors

Visual tracking has become an increasingly important topic of research in the field of Computer Vision (CV). There are currently many tracking methods based on the Detect-then-Track paradigm. This type of approach may allow for a system to track a random object with just one initialization phase, but may often rely on constructing models to follow the object. Another limitation of these methods is that they are computationally and memory intensive, which hinders their application to resource constrained platforms such as mobile devices. Under these conditions, the implementation of Augmented Reality (AR) or complex multi-part systems is not possible. In this thesis, we explore a variety of interest point descriptors for generic object tracking. The SIFT descriptor is considered a benchmark and will be compared with binary descriptors such as BRIEF, ORB, BRISK, and FREAK. The accuracy of these descriptors is benchmarked against the ground truth of the object\u27s location. We use dictionaries of descriptors to track regions with small error under variations due to occlusions, illumination changes, scaling, and rotation. This is accomplished by using Dense-to-Sparse Search Pattern, Locality Constraints, and Scale Adaptation. A benchmarking system is created to test the descriptors\u27 accuracy, speed, robustness, and distinctness. This data offers a comparison of the tracking system to current state of the art systems such as Multiple Instance Learning Tracker (MILTrack), Tracker Learned Detection (TLD), and Continuously Adaptive MeanShift (CAMSHIFT)