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This paper is intended to recognize and track objects on real time basis under the embedded environment. Speed and rate of recognition have been improved by strong directional SURF and optical flow, but its speed is not fast enough for the embedded system. In this paper, recognition and tracking objects by integration of ORB and optical flow with a parallelization using OpenMP are implemented. The result shows an improved processing speed.

This paper proposes a panoramic image stitching that operates in real time at the embedded environment by applying ROI and PROSAC algorithm. The conventional panoramic image stitching applies SURF or SIFT algorithm which contains complicated operations and a lots of data, at the overall image to detect feature points.Also it applies RANSAC algorithm to remove outliers, so that an additional verification time is required due to its randomness. In this paper, unnecessary data are eliminated by setting ROI based on the characteristics of panorama images, and PROSAC algorithm is applied for removing outliers to reduce verification time. The proposed method was implemented on the ORDROID-XU board with ARM Cortex-A15. The result shows an improvement of about 54% in the processing time compared to the conventional method.

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