An efficient algorithm is proposed for the adaptive sizing of a tracking window in correlation-based video tracking. Since a tracking window specifies a support region when estimating a target displacement, the ability to adapt the window size relative to a moving target significantly influences the performance of video tracking. The basic strategy of the proposed algorithm is to maintain the occupancy rate of the target in the tracking window within a specified range. As such, the proposed algorithm measures the occupancy rate using the ratio of the power of the spatial gradients in the edge subwindows, which edge the tracking window, to that in the tracking window. In addition, the level of any complex background and additive white noise is also evaluated to reduce their effect on the gradients. Experimental results using various artificial and real sequences confirm that the proposed algorithm can effectively adjust a tracking window to a moving target and is robust to a complex background and noise.
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Jae Gon SON, Chae Whan LIM, Il CHOI, Nam Chul KIM, "Adaptive Sizing of Tracking Window for Correlation-Based Video Tracking" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 6, pp. 1015-1021, June 2002, doi: .
Abstract: An efficient algorithm is proposed for the adaptive sizing of a tracking window in correlation-based video tracking. Since a tracking window specifies a support region when estimating a target displacement, the ability to adapt the window size relative to a moving target significantly influences the performance of video tracking. The basic strategy of the proposed algorithm is to maintain the occupancy rate of the target in the tracking window within a specified range. As such, the proposed algorithm measures the occupancy rate using the ratio of the power of the spatial gradients in the edge subwindows, which edge the tracking window, to that in the tracking window. In addition, the level of any complex background and additive white noise is also evaluated to reduce their effect on the gradients. Experimental results using various artificial and real sequences confirm that the proposed algorithm can effectively adjust a tracking window to a moving target and is robust to a complex background and noise.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_6_1015/_p
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@ARTICLE{e85-d_6_1015,
author={Jae Gon SON, Chae Whan LIM, Il CHOI, Nam Chul KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Sizing of Tracking Window for Correlation-Based Video Tracking},
year={2002},
volume={E85-D},
number={6},
pages={1015-1021},
abstract={An efficient algorithm is proposed for the adaptive sizing of a tracking window in correlation-based video tracking. Since a tracking window specifies a support region when estimating a target displacement, the ability to adapt the window size relative to a moving target significantly influences the performance of video tracking. The basic strategy of the proposed algorithm is to maintain the occupancy rate of the target in the tracking window within a specified range. As such, the proposed algorithm measures the occupancy rate using the ratio of the power of the spatial gradients in the edge subwindows, which edge the tracking window, to that in the tracking window. In addition, the level of any complex background and additive white noise is also evaluated to reduce their effect on the gradients. Experimental results using various artificial and real sequences confirm that the proposed algorithm can effectively adjust a tracking window to a moving target and is robust to a complex background and noise.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - Adaptive Sizing of Tracking Window for Correlation-Based Video Tracking
T2 - IEICE TRANSACTIONS on Information
SP - 1015
EP - 1021
AU - Jae Gon SON
AU - Chae Whan LIM
AU - Il CHOI
AU - Nam Chul KIM
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2002
AB - An efficient algorithm is proposed for the adaptive sizing of a tracking window in correlation-based video tracking. Since a tracking window specifies a support region when estimating a target displacement, the ability to adapt the window size relative to a moving target significantly influences the performance of video tracking. The basic strategy of the proposed algorithm is to maintain the occupancy rate of the target in the tracking window within a specified range. As such, the proposed algorithm measures the occupancy rate using the ratio of the power of the spatial gradients in the edge subwindows, which edge the tracking window, to that in the tracking window. In addition, the level of any complex background and additive white noise is also evaluated to reduce their effect on the gradients. Experimental results using various artificial and real sequences confirm that the proposed algorithm can effectively adjust a tracking window to a moving target and is robust to a complex background and noise.
ER -