Performance evaluation of marker recognition algorithm for mobile augmented reality in the real environment

Abstract

The ultimate goals of Augmented Reality (AR) applications are to provide a better management and ubiquitous access to information using seamless techniques in which the interactive real world is combined with an interactive computer-generated world, creating one coherent environment. The performance of the AR algorithm in terms of brightness, rotation, and scale in the real environment had been addressed as an issue due to the controlled environment. Hence, a performance evaluation of the AR algorithm in a real controlled environment is proposed in this paper. BRISK detector, FREAK descriptor, and Hamming distance matcher algorithms are implemented in a mobile AR application in order to evaluate the AR algorithm. The mobile AR application is run on the Samsung Note 9 smartphone. The image used in the evaluation is the Graffiti image from the Mikolajczyk data set. Graffiti image is fully printed in an A4 size paper and is attached to a wall with a height of 1.5m. This performance evaluation is able to evaluate the robustness of the AR algorithm in terms of brightness value from 0 Watts per square meter up to 70 Watts per square meter. The robustness of the AR algorithm in terms of scale invariance was evaluated from the distance of 5cm up to 50cm from the input image. The AR algorithm can obtain an accuracy of 83.49%, 70.89%, and 72.78% in terms of brightness changes, scale changes, and rotation changes respectively. This work introduces a more suitable performance evaluation for an AR application in a real controlled environment.