Object boundary edge selection for human body tracking using level-of-detail canny edges

Tae Yong Kim, Jihun Park, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We propose a method for an accurate subject tracking by selecting only tracked subject boundary edges in a video stream with changing background and a moving camera. Our boundary edge selection is done in two steps; 1) remove background edges using an edge motion, 2) from the output of the previous step, select boundary edges using a normal direction derivative of the tracked contour. Our accurate tracking is based on reducing affects from irrelevant edges by selecting boundary edge pixels only. In order to remove background edges using the edge motion, we compute tracked subject motion and edge motions. The edges with different motion direction than the subject motion are removed. In selecting boundary edges using the contour normal direction, we compute image gradient values on every edge pixels, and select edge pixels with large gradient values. We use multi-level Canny edge maps to get proper details of a scene. Multi-level edge maps allow us robust tracking even though the tracked object boundary is not clear, because we can adjust the detail level of an edge map for the scene. The computed contour is improved by checking against a strong (simple) Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsC. Zhang, H.W. Guesgen, W.K. Yeap
Pages787-796
Number of pages10
Volume3157
Publication statusPublished - 2004
Event8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence - Auckland, New Zealand
Duration: 2004 Aug 92004 Aug 13

Other

Other8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence
CountryNew Zealand
CityAuckland
Period04/8/904/8/13

Fingerprint

Pixels
Cameras
Derivatives
Costs

ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Kim, T. Y., Park, J., & Lee, S. W. (2004). Object boundary edge selection for human body tracking using level-of-detail canny edges. In C. Zhang, H. W. Guesgen, & W. K. Yeap (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3157, pp. 787-796)

Object boundary edge selection for human body tracking using level-of-detail canny edges. / Kim, Tae Yong; Park, Jihun; Lee, Seong Whan.

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / C. Zhang; H.W. Guesgen; W.K. Yeap. Vol. 3157 2004. p. 787-796.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, TY, Park, J & Lee, SW 2004, Object boundary edge selection for human body tracking using level-of-detail canny edges. in C Zhang, HW Guesgen & WK Yeap (eds), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). vol. 3157, pp. 787-796, 8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence, Auckland, New Zealand, 04/8/9.
Kim TY, Park J, Lee SW. Object boundary edge selection for human body tracking using level-of-detail canny edges. In Zhang C, Guesgen HW, Yeap WK, editors, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). Vol. 3157. 2004. p. 787-796
Kim, Tae Yong ; Park, Jihun ; Lee, Seong Whan. / Object boundary edge selection for human body tracking using level-of-detail canny edges. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). editor / C. Zhang ; H.W. Guesgen ; W.K. Yeap. Vol. 3157 2004. pp. 787-796
@inproceedings{9e008fd2e2ff4f73b831fdfb504362c2,
title = "Object boundary edge selection for human body tracking using level-of-detail canny edges",
abstract = "We propose a method for an accurate subject tracking by selecting only tracked subject boundary edges in a video stream with changing background and a moving camera. Our boundary edge selection is done in two steps; 1) remove background edges using an edge motion, 2) from the output of the previous step, select boundary edges using a normal direction derivative of the tracked contour. Our accurate tracking is based on reducing affects from irrelevant edges by selecting boundary edge pixels only. In order to remove background edges using the edge motion, we compute tracked subject motion and edge motions. The edges with different motion direction than the subject motion are removed. In selecting boundary edges using the contour normal direction, we compute image gradient values on every edge pixels, and select edge pixels with large gradient values. We use multi-level Canny edge maps to get proper details of a scene. Multi-level edge maps allow us robust tracking even though the tracked object boundary is not clear, because we can adjust the detail level of an edge map for the scene. The computed contour is improved by checking against a strong (simple) Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.",
author = "Kim, {Tae Yong} and Jihun Park and Lee, {Seong Whan}",
year = "2004",
language = "English",
volume = "3157",
pages = "787--796",
editor = "C. Zhang and H.W. Guesgen and W.K. Yeap",
booktitle = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

}

TY - GEN

T1 - Object boundary edge selection for human body tracking using level-of-detail canny edges

AU - Kim, Tae Yong

AU - Park, Jihun

AU - Lee, Seong Whan

PY - 2004

Y1 - 2004

N2 - We propose a method for an accurate subject tracking by selecting only tracked subject boundary edges in a video stream with changing background and a moving camera. Our boundary edge selection is done in two steps; 1) remove background edges using an edge motion, 2) from the output of the previous step, select boundary edges using a normal direction derivative of the tracked contour. Our accurate tracking is based on reducing affects from irrelevant edges by selecting boundary edge pixels only. In order to remove background edges using the edge motion, we compute tracked subject motion and edge motions. The edges with different motion direction than the subject motion are removed. In selecting boundary edges using the contour normal direction, we compute image gradient values on every edge pixels, and select edge pixels with large gradient values. We use multi-level Canny edge maps to get proper details of a scene. Multi-level edge maps allow us robust tracking even though the tracked object boundary is not clear, because we can adjust the detail level of an edge map for the scene. The computed contour is improved by checking against a strong (simple) Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

AB - We propose a method for an accurate subject tracking by selecting only tracked subject boundary edges in a video stream with changing background and a moving camera. Our boundary edge selection is done in two steps; 1) remove background edges using an edge motion, 2) from the output of the previous step, select boundary edges using a normal direction derivative of the tracked contour. Our accurate tracking is based on reducing affects from irrelevant edges by selecting boundary edge pixels only. In order to remove background edges using the edge motion, we compute tracked subject motion and edge motions. The edges with different motion direction than the subject motion are removed. In selecting boundary edges using the contour normal direction, we compute image gradient values on every edge pixels, and select edge pixels with large gradient values. We use multi-level Canny edge maps to get proper details of a scene. Multi-level edge maps allow us robust tracking even though the tracked object boundary is not clear, because we can adjust the detail level of an edge map for the scene. The computed contour is improved by checking against a strong (simple) Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. Our experimental results show that our tracking approach is robust enough to handle a complex-textured scene.

UR - http://www.scopus.com/inward/record.url?scp=22944433477&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=22944433477&partnerID=8YFLogxK

M3 - Conference contribution

VL - 3157

SP - 787

EP - 796

BT - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

A2 - Zhang, C.

A2 - Guesgen, H.W.

A2 - Yeap, W.K.

ER -