Enhanced Augmented Reality Framework for Sports Entertainment Applications

Augmented Reality (AR) superimposes virtual information on real-world data, such as displaying useful information on videos/images of a scene. This dissertation presents an Enhanced AR (EAR) framework for displaying useful information on images of a sports game. The challenge in such applications is robust object detection and recognition. This is even more challenging when there is strong sunlight. We address the phenomenon where a captured image is degraded by strong sunlight. The developed framework consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player detection, face detection, recognition of players, and display of personal information of players. First, an algorithm based on Multi-Scale Retinex (MSR) is proposed for image enhancement. For the tasks of player and face detection, we use adaptive boosting algorithm with Haar-like features for both feature selection and classification. The player face recognition algorithm uses adaptive boosting with the LDA for feature selection and nearest neighbor classifier for classification. The framework can be deployed in any sports where a viewer captures images. Display of players-specific information enhances the end-user experience. Detailed experiments are performed on 2096 diverse images captured using a digital camera and smartphone. The images contain players in different poses, expressions, and illuminations. Player face recognition module requires players faces to be frontal or up to ?350 of pose variation. The work demonstrates the great potential of computer vision based approaches for future development of AR applications.COMSATS Institute of Information Technolog

Hybrid differential evolution algorithms for the optimal camera placement problem

Purpose – This paper investigates to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem. Design/methodology/approach – This problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, a greedy algorithm, and a row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera in order to find better solutions. Findings – The experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice. Originality/value – Up to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations in order to fully benefit from the DE mutation scheme

Facial identification from online images for use in the prevention of child trafficking and exploitation

Every year, an estimated 1.2 million children are trafficked (International Labour Office, 2002). The National Center for Missing & Exploited Children (NCMEC) received a 432% increase in child sexual abuse images for the purposes of identification between 2005 and 2009 (U.S. Department of Justice, 2010), and they assisted in the identification of 2,589 victims related to indecent images of children in 2015 (NCMEC, 2015a). In relation to the vast number of images received, machine-based facial recognition could help law enforcement and other organisations to match faces more efficiently. The use of facial recognition technology has become more popular within our society, but where rapid juvenile growth changes facial features recognition is challenging, especially for children under 15 years of age with changes to the outer contour of the face (Ramanathan and Chellappa, 2006). The challenge not only relates to the growth of the child’s face, but also relates to face recognition in the wild with unconstrained images. This study aims to provide an open-access database of facial images, documenting the different stages of facial growth from numerous individuals from birth to 19 years of age. There are currently very limited longitudinal databases available for the research community, and the collection of this database will benefit all researchers who wish to study age progression and facial growth. Ferguson (2015) suggested that facial recognition algorithms can perform better than humans in the identification of faces of children. Experiment 1 of this research takes a further step to explore how the difference in age group and age gap can affect the recognition rate using various facial recognition software, and explores the possibilities of group tagging. Results indicated that the use of multiple images is beneficial for the facial identification of children. Experiment 2 explores whether age progression work could further improve the recognition rate of juvenile faces. This study documents the workflow of a new method for digital manual age progression using a combination of previously published methods. The proposed age progression method for children recorded satisfactory levels of repeatability with facial measurements at the Nasion (n) and Trichion (tr) showing the most inaccuracy. No previous studies have tested how different conditions (i.e. blurring, resolution reduction, cropping and black and white) can affect machine-based facial recognition nor have they explored the relationship between age progression images and facial recognition software. The study found that reduction of the resolution of an age progression image improves automated facial recognition for juvenile identification, and manual age progressions are no more useful than the original image for facial identification of missing children. The outcome of this research directly benefits those who practice facial identification in relation to children, especially for age progression casework

Effects of pose and image resolution on automatic face recognition

The popularity of face recognition systems have increased due to their use in widespread applications. Driven by the enormous number of potential application domains, several algorithms have been proposed for face recognition. Face pose and image resolutions are among the two important factors that influence the performance of face recognition algorithms. In this study, the authors present a comparative study of three baseline face recognition algorithms to analyse the effects of two aforementioned factors. The algorithms studied include (a) the adaptive boosting (AdaBoost) with linear discriminant analysis as weak learner, (b) the principal component analysis (PCA)-based approach, and (c) the local binary pattern (LBP)-based approach. They perform an empirical study using the images with systematic pose variation and resolution from multi-pose, illumination, and expression database to explore the recognition accuracy. This evaluation is useful for practical applications because most engineers start development of a face recognition application using these baseline algorithms. Simulation results revealed that the PCA is more accurate in classifying the pose variation, whereas the AdaBoost is more robust in identifying low-resolution images. The LBP does not classify face images of size 20 × 20 pixels and below and has lower recognition accuracy than PCA and AdaBoost