The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

RGB-D-based Action Recognition Datasets: A Survey

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols

Augmented Reality for Information Kiosk

Nowadays people widely use internet for purchasing a home, car, furniture etc.  In order to obtain information for purchasing that product user prefer advertisements, pamphlets, and various sources or obtain the information by means of Salesperson. Though, to receiving such product information on computer or any device, users have to use  lots of mouse and keyboard actions again and again, which is wastage of time and inconvenience. This will reduce the amount of time to gather particular information regarding the particular product. User is also unable to determine its inner dimensions through images. These dimensions can be predicted by using 3D motion tracking of human movements and Augmented Reality. Based on 3D motion tracking of human movements and Augmented Reality application, we introduce a such kind of interaction that is not seen before . In the proposed system, the main aim is to demonstrate that with better interaction features in showrooms as well as online shopping could improve sales by demonstrating the purchasing item more wider. With the help of the our project the customer will be able to view his choices on screen according to him and thereby can make better decisions. In this paper, we proposed hand gesture detection and recognition method to detect hand movements , and then through the hand gestures, control commands are sent to the system that enable user to retrieve data and access from Information Kiosk for better purchase decision. Keywords: 3D motion tracking, Augmented Reality, Hand Gestures, Information Kiosk. Introductio

Augmented Reality for Information Kiosk

Nowadays people widely use internet for purchasing a home, car, furniture etc.  In order to obtain information for purchasing that product user prefer advertisements, pamphlets, and various sources or obtain the information by means of Salesperson. Though, to receiving such product information on computer or any device, users have to use  lots of mouse and keyboard actions again and again, which is wastage of time and inconvenience. This will reduce the amount of time to gather particular information regarding the particular product. User is also unable to determine its inner dimensions through images. These dimensions can be predicted by using 3D motion tracking of human movements and Augmented Reality. Based on 3D motion tracking of human movements and Augmented Reality application, we introduce a such kind of interaction that is not seen before . In the proposed system, the main aim is to demonstrate that with better interaction features in showrooms as well as online shopping could improve sales by demonstrating the purchasing item more wider. With the help of the our project the customer will be able to view his choices on screen according to him and thereby can make better decisions. In this paper, we proposed hand gesture detection and recognition method to detect hand movements , and then through the hand gestures, control commands are sent to the system that enable user to retrieve data and access from Information Kiosk for better purchase decision. Keywords: 3D motion tracking, Augmented Reality, Hand Gestures, Information Kiosk. Introduction

Gesture based interface for image annotation

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaGiven the complexity of visual information, multimedia content search presents more problems than textual search. This level of complexity is related with the difficulty of doing automatic image and video tagging, using a set of keywords to describe the content. Generally, this annotation is performed manually (e.g., Google Image) and the search is based on pre-defined keywords. However, this task takes time and can be dull. In this dissertation project the objective is to define and implement a game to annotate personal digital photos with a semi-automatic system. The game engine tags images automatically and the player role is to contribute with correct annotations. The application is composed by the following main modules: a module for automatic image annotation, a module that manages the game graphical interface (showing images and tags), a module for the game engine and a module for human interaction. The interaction is made with a pre-defined set of gestures, using a web camera. These gestures will be detected using computer vision techniques interpreted as the user actions. The dissertation also presents a detailed analysis of this application, computational modules and design, as well as a series of usability tests