International Science Index


151

A Study on Algorithm Fusion for Recognition and Tracking of Moving Robot

Abstract:This paper presents an algorithm for the recognition and tracking of moving objects, 1/10 scale model car is used to verify performance of the algorithm. Presented algorithm for the recognition and tracking of moving objects in the paper is as follows. SURF algorithm is merged with Lucas-Kanade algorithm. SURF algorithm has strong performance on contrast, size, rotation changes and it recognizes objects but it is slow due to many computational complexities. Processing speed of Lucas-Kanade algorithm is fast but the recognition of objects is impossible. Its optical flow compares the previous and current frames so that can track the movement of a pixel. The fusion algorithm is created in order to solve problems which occurred using the Kalman Filter to estimate the position and the accumulated error compensation algorithm was implemented. Kalman filter is used to create presented algorithm to complement problems that is occurred when fusion two algorithms. Kalman filter is used to estimate next location, compensate for the accumulated error. The resolution of the camera (Vision Sensor) is fixed to be 640x480. To verify the performance of the fusion algorithm, test is compared to SURF algorithm under three situations, driving straight, curve, and recognizing cars behind the obstacles. Situation similar to the actual is possible using a model vehicle. Proposed fusion algorithm showed superior performance and accuracy than the existing object recognition and tracking algorithms. We will improve the performance of the algorithm, so that you can experiment with the images of the actual road environment.
References:
[1] H. Bay, E. Andreas, T. Tuytelaars and L. V. Gool, "Speeded-up robust features", Computer Vision and Image Understanding, Vol 110, Issue 3, pp 346-359, June 2008.
[2] Gary Bradski and Adrian Kaehler, "Learning OpenCV" O-REILLY, pp. 322-329, 2009.
[3] Lindeberg, "Feature detection with automatic scale selection," IJCV. 1998.
[4] D. G. Lowe, "Distinctive image features from scale invariant keypoints", International Journal of Computer Vision, Vol. 60, No. 2 pp. 91-110, 2004.
[5] Parah H. Batavia, Dean A. Pomerleau and Chuck Thorpe, "Evertaking Vehicle Detection using Implicit Optical Flow", Proceedings of the IEEE Transportation Systems Conference, pp. 729-734, 1997.
[6] Konstantinos G. Derpainis, "The Harris Corner Detector", Cot, 2004.
[7] E. Bublee, C. Rabaud, K. Konolige, and G.Bradski, "ORB: an efficient alternative to SIFT or SURF", International Conference on Computer Vision, Nov, 2011.
[8] John G. Allen, Richard Y. D. Xu and Jesse S. Jin, "Object tracking using CamShift algorithm and multiple quantized feature spaces" In ACM International Conference Proceeding Series; Vol 100, pp.3~7, 2004.
[9] U. C. Jung, S. H. Jin, X. D. Pham, J. W. Jeon, J. E. Byun, H. Kang, "A real-time object tracking system using a particle filter", 2006 IEEE/ RSJ Int. Conf. vol. 9, pp. 2822~2827, 2006. 10.
[10] H. W. Sorenson, "Least-square estimation:from Gauss to Kalman", IEEE Spectrum, vol. 7. pp. 63~68, July 1970.