반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

모바일 환경 Homography를 이용한 특징점 기반 다중 객체 추적

Multi-Object Tracking Based on Keypoints Using Homography in Mobile Environments

  • Han, Woo ri (Dankook University of Electronic Engineering, Dankook University) ;
  • Kim, Young-Seop (Dankook University of Electronic Engineering, Dankook University) ;
  • Lee, Yong-Hwan (Department of Smart Mobile, Far East University)
  • 투고 : 2015.09.04
  • 심사 : 2015.09.22
  • 발행 : 2015.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper proposes an object tracking system based on keypoints using homography in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information. Tracking module tracks an object using homography information that generate by being matched on the learned object keypoints to the current object keypoints. Then update the window included the object for defining object's pose. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track objects with updating object's pose for the use of mobile platform.

키워드

참고문헌

  1. Lee, S.-G., "Survey on mixed-reality R&D," Journal of the Korea Computer Graphics Society, 13(2), pp. 1-15, (2007).
  2. Harris, C. and Stephens, M., "A combined corner and edge detector," Proc. Alvey Vision Conf., pp. 147-151, 1998.
  3. Lindeberg, T., "Feature detection with automatic scale selection," International Journal of Computer Vision, 30(3), pp. 79-116, (1998). https://doi.org/10.1023/A:1008045108935
  4. Kitchen, L. and Rosenfeld, A., "Gray level corner detection," Pattern Recognition Letters, 1(2), pp. 95-102, (1982). https://doi.org/10.1016/0167-8655(82)90020-4
  5. Smith, S. M. and Brady, J. M., "SUSAN-A new approach to low level image processing," International Journal of Computer Vision, 23(1), pp. 45-78, (1997). https://doi.org/10.1023/A:1007963824710
  6. Lowe, D., "Distinctive image feature from scaleinvariant keypoints," International Journal of Computer Vision, 60(2), pp. 91-110, (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94
  7. Bay, H., Tuytelaars, T. and Cool, L. V., "SURF: speeded up robust features," ECCV, 3951, pp. 404-417, (2006).
  8. Lee, H. J., Lee, S.-G., "Improvement method of tracking speed for color object using Kalman filter and SURF," Journal of Korea Multimedia Society, 15(3), pp. 336-344, (2012). https://doi.org/10.9717/kmms.2012.15.3.336
  9. Do, Y.-S. and Jeong, Y.-J., "Hardware design of SURF-based feature extraction and description for object tracking," Journal of The Institute of Electronics Engineers of Korea, 50(5), (2013).
  10. Lee, Y.-H., Park, J.-H. and Kim, Y.-S., "Comparative analysis of the performance of SIFT and SURF," Journal of the Semiconductor & Display Technology, 12(3), pp. 59-64, (2013).
  11. Ji-Won Hong, Byung-Moon, Sang-Wook Kim., "A study on LSH parameters for large multimedia databases," The Korea Contents Association, 2015(5), (2015).
  12. Choi, J. and Cho, Y., "Moving object recognition and tracking algorithm using parallel processing of SURF and optical flow," Proceedings of KIIS Fall Conference, 21(2), (2011).
  13. Cho, T.-H. and Kang, H.-M., "Gaze tracking using a modified starburst algorithm and homography normalization," Journal of the Korea Institute of Information and Communication Engineering, 18(5), pp. 1162-1170, (2014). https://doi.org/10.6109/jkiice.2014.18.5.1162