Performance Improvement of Moving Object Recognition and Tracking Algorithm using Parallel Processing of SURF and Optical Flow
The paper proposes a way of parallel processing of
SURF and Optical Flow for moving object recognition and tracking.
The object recognition and tracking is one of the most important task
in computer vision, however disadvantage are many operations cause
processing speed slower so that it can-t do real-time object recognition
and tracking. The proposed method uses a typical way of feature
extraction SURF and moving object Optical Flow for reduce
disadvantage and real-time moving object recognition and tracking,
and parallel processing techniques for speed improvement. First
analyse that an image from DB and acquired through the camera using
SURF for compared to the same object recognition then set ROI
(Region of Interest) for tracking movement of feature points using
Optical Flow. Secondly, using Multi-Thread is for improved
processing speed and recognition by parallel processing. Finally,
performance is evaluated and verified efficiency of algorithm
throughout the experiment.
[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] J. H. Duncan, and T. C. Chou, "Temporal edges: The detection of motion
and the computation of optical flow," in Proc. IEEE 2nd int. Conf.
Computer Vision, Florida, USA, Dec. 1988, pp.374-382
[7] S. Denman, V. Chandran, and S. Sridharan, "Adaptive Optical Flow for
Person Tracking," in Proc. Digital Image Computing: Techniques and
Applications, Caims, Australia, Dec. 2005, pp. 44-50
[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] J. H. Duncan, and T. C. Chou, "Temporal edges: The detection of motion
and the computation of optical flow," in Proc. IEEE 2nd int. Conf.
Computer Vision, Florida, USA, Dec. 1988, pp.374-382
[7] S. Denman, V. Chandran, and S. Sridharan, "Adaptive Optical Flow for
Person Tracking," in Proc. Digital Image Computing: Techniques and
Applications, Caims, Australia, Dec. 2005, pp. 44-50
@article{"International Journal of Information, Control and Computer Sciences:49403", author = "Jungho Choi and Youngwan Cho", title = "Performance Improvement of Moving Object Recognition and Tracking Algorithm using Parallel Processing of SURF and Optical Flow", abstract = "The paper proposes a way of parallel processing of
SURF and Optical Flow for moving object recognition and tracking.
The object recognition and tracking is one of the most important task
in computer vision, however disadvantage are many operations cause
processing speed slower so that it can-t do real-time object recognition
and tracking. The proposed method uses a typical way of feature
extraction SURF and moving object Optical Flow for reduce
disadvantage and real-time moving object recognition and tracking,
and parallel processing techniques for speed improvement. First
analyse that an image from DB and acquired through the camera using
SURF for compared to the same object recognition then set ROI
(Region of Interest) for tracking movement of feature points using
Optical Flow. Secondly, using Multi-Thread is for improved
processing speed and recognition by parallel processing. Finally,
performance is evaluated and verified efficiency of algorithm
throughout the experiment.", keywords = "moving object recognition, moving object tracking,
SURF, Optical Flow, Multi-Thread.", volume = "6", number = "2", pages = "160-4", }