EAR: Enhanced Augmented Reality System for Sports Entertainment Applications

Abstract

Augmented Reality (AR) overlays virtual information on real world data, such as displaying useful information on videos/images of a scene. This paper presents an Enhanced AR (EAR) system that displays useful statistical players' information on captured images of a sports game. We focus on the situation where the input image is degraded by strong sunlight. Proposed EAR system consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player and face detection, face recognition, and players' statistics display. First, an algorithm based on multi-scale retinex is proposed for image enhancement. Then, to detect players' and faces', we use adaptive boosting and Haar features for feature extraction and classification. The player face recognition algorithm uses boosted linear discriminant analysis to select features and nearest neighbor classifier for classification. The system can be adjusted to work in different types of sports where the input is an image and the desired output is display of information nearby the recognized players. Simulations are carried out on 2096 different images that contain players in diverse conditions. Proposed EAR system demonstrates the great potential of computer vision based approaches to develop AR applications.

Augmented reality tools for sports education and training

“…For example, S. Lee, Ahn, Hwang, and Kim (2011) implemented a vision-based AR system that displayed supplementary information about the position of each player during the baseball game. Such algorithms could identify players during matches and address the comments to them during debrief sessions (Mahmood et al, 2017). Figure 5: Adding information using virtual elements might improve viewers' experience.…”

“…However, Kinect's limited application for complex movements, such as standing techniques in Judo stems from its inability to see the body parts in the optical shade (Sieluzycki et al, 2016). This limitation can be resolved with machine learning and computer vision algorithms that could be used to improve the accuracy of such systems (Mahmood et al, 2017). In any case, the accuracy of AR systems must be tested since the human visual system can detect slight shifts between the virtual image and the targeted actual display location.…”

Systematic review and meta-analysis of augmented reality in medicine, retail, and games

“…It has been shown that robotic cameramen facilitate more precise and advanced interaction with virtual elements, and through zooming and multiple angle views, improve the performance of AR broadcasting in all sorts of environments. An enhanced AR system displays statistical players' information on captured images of a sports game [41]. It is an image enhancement technique based on an algorithm that implements multi-scale retinex.…”

Automatic measurement of the traffic sign with digital segmentation and recognition

“…RGB is most natural colour space but more sensitive to change in lights [18]. Owing to more nearest to human colour perception, hue, saturation, and value (HSV) or hue, intensity, and saturation [19] are more popular. The colour space is also less affected by illumination, but its computational cost is high.…”