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A Fast Snake Algorithm for Tracking Multiple Objects

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초록

A Snake is an active contour for representing object contours. Traditional snake algorithms are often used to represent the contour of a single object. However, if there is more than one object in the image, the snake model must be adaptive to determine the corresponding contour of each object. Also, the previous initialized snake contours risk getting the wrong results when tracking multiple objects in successive frames due to the weak topology changes. To overcome this problem, in this paper, we present a new snake method for efficiently tracking contours of multiple objects. Our proposed algorithm can provide a straightforward approach for snake contour rapid splitting and connection, which usually cannot be gracefully handled by traditional snakes. Experimental results of various test sequence images with multiple objects have shown good performance, which proves that the proposed method is both effective and accurate.

Abstract

A Snake is an active contour for representing object contours. Traditional snake algorithms are often used to represent the contour of a single object. However, if there is more than one object in the image, the snake model must be adaptive to determine the corresponding contour of each object. Also, the previous initialized snake contours risk getting the wrong results when tracking multiple objects in successive frames due to the weak topology changes. To overcome this problem, in this paper, we present a new snake method for efficiently tracking contours of multiple objects. Our proposed algorithm can provide a straightforward approach for snake contour rapid splitting and connection, which usually cannot be gracefully handled by traditional snakes. Experimental results of various test sequence images with multiple objects have shown good performance, which proves that the proposed method is both effective and accurate.

참고문헌 (12)

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  3. S. H. Kim, "Snake-Based Contour Detection for Objects with Boundary Concavities", Optical Engineering, SPIE, 47(3), pp.037002-1~037002-7 (2008)
  4. 김신형, "스테레오 동영상에서 스네이크를 이용한 객체윤곽 추적 알고리즘", 정보처리학회논문지. 소프트웨어 및 데이터 공학, 12(7), pp.767~774 (2005), 한국정보처리학회
  5. 윤희병, "이동물체 탐지 및 추적을 위한 에너지 보정 스네이크(ECS) 알고리즘의 실험 및 평가", 정보처리학회논문지. 소프트웨어 및 데이터 공학, 16(4), pp.289~298 (2009), 한국정보처리학회
  6. De Vylder, "Tracking Multiple Objects Using Moving Snakes", 16th IEEE ICDSP 2009, pp.1~6 (2009)
  7. V. Caselles, "Geodesic Active Contours", International Journal of Computer Vision, 22, pp.61~79 (1997)
  8. T. Chan, "Active Contours Without Edges", IEEE Transaction on Image Processing, 10, pp.266~277 (2001)
  9. A. C. Li, "Level Set Evolution Without Re-initialization: A New Variational Formulation", CVPR 2005, 통권 : 1, pp.430~436 (2005)
  10. H. Shan, "Curvelet-Based Geodesic Snake for Image Segmentation with Multiple Objects", Pattern Recognition Letters, 31, pp.355~360 (2010)
  11. T. Srinark, "A Framework for Multiple Snakes and Its Applications", Pattern Recognition Society, 39, pp.1555~1565 (2006)
  12. David J. Fleet, "Optical Flow Estimation. In: Mathematical Models in Computer Vision, In The Handbook, Chater15", pp.239~258 (2005), Springer

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