Augmented Reality is an advance technology that enhances the real world by overlaying digital data on top of it. When Augmented Reality (AR) experience is delivered on mobile devices it is termed as mobile augmented reality (MAR). MAR is state-of-the-art technology that has completely revolutionized the way of accessing and interacting with information thus invoking new experiences for users all around the world. This article is an effort to summarize the current research regarding user experience of MAR. Mobile AR Publications of past 10 years are identified for a preliminary review from prominent online databases and digital libraries. The aim of this study is to identify the areas of User Experience (UX) that lack research. We present a classification of present UX research in MAR domain. Research findings and possible opportunities for future research are also discussed.

Automatic human motion tracking in video sequences is one of the most frequently tackled tasks in computer vision community. The goal of human motion capture is to estimate the joints angles of human body at any time. However, this is one of the most challenging problem in computer vision and pattern recognition due to the high-dimensional search space, self-occlusion, and high variability in human appearance. Several approaches have been proposed in the literature using different techniques. However, conventional approaches such as stochastic particle filtering have shortcomings in computational cost, slowness of convergence, suffers from the curse of dimensionality and demand a high number of evaluations to achieve accurate results. Particle swarm optimization (PSO) is a population-based globalized search algorithm which has been successfully applied to address human motion tracking problem and produced better results in high-dimensional search space. This paper presents a systematic literature survey on the PSO algorithm and its variants to human motion tracking. An attempt is made to provide a guide for the researchers working in the field of PSO based human motion tracking from video sequences. Additionally, the paper also presents the performance of various model evaluation search strategies within PSO tracking framework for 3D pose tracking.

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