Improving performance on object recognition for real-time on mobile devices

Augmented reality has been on the rise due to the proliferation of mobile devices. At the same time, object recognition has also come to the fore. In particular, many studies have focused on object recognition based on markerless matching. However, most of these studies have focused on desktop systems, which can have high performance in terms of CPU and memory, rather than investigating the use of mobile systems, which have been previously unable to provide high-performance object recognition based on markerless matching. In this paper, we propose a method that uses the OpenCV mobile library to improve real-time object recognition performance on mobile systems. First, we investigate the original object recognition algorithm to identify performance bottlenecks. Second, we optimize the algorithm by analyzing each module and applying appropriate code enhancements. Last, we change the operational structure of the algorithm to improve its performance, changing the execution frequency of the object recognition task from every frame to every four frames for real-time operation. During the three frames in which the original method is not executed, the object is instead recognized using the mobile devices accelerometer. We carry out experiments to reveal how much each aspect of our method improves the overall object recognition performance; overall, experimental performance improves by approximately 800 %, with a corresponding reduction of approximately 1 % in object recognition accuracy. Therefore, the proposed technique can be used to significantly improve the performance of object recognition based on markerless matching on mobile systems for real-time operation.