TY - GEN
T1 - Robust visual tracking via transfer learning
AU - Luo, Wenhan
AU - Li, Xi
AU - Li, Wei
AU - Hu, Weiming
PY - 2011
Y1 - 2011
N2 - In this paper, we propose a boosting based tracking framework using transfer learning. To deal with complex appearance variations, the proposed tracking framework tries to utilize discriminative information from previous frames to conduct the tracking task in the current frame, and thus transfers some prior knowledge from the previous source data domain to the current target data domain, resulting in a high discriminative tracker for distinguishing the object from the background. The proposed tracking system has been tested on several challenging sequences. Experimental results demonstrate the effectiveness of the proposed tracking framework.
AB - In this paper, we propose a boosting based tracking framework using transfer learning. To deal with complex appearance variations, the proposed tracking framework tries to utilize discriminative information from previous frames to conduct the tracking task in the current frame, and thus transfers some prior knowledge from the previous source data domain to the current target data domain, resulting in a high discriminative tracker for distinguishing the object from the background. The proposed tracking system has been tested on several challenging sequences. Experimental results demonstrate the effectiveness of the proposed tracking framework.
KW - boosting
KW - tracking
KW - transfer learning
UR - https://openalex.org/W1965525826
UR - https://www.scopus.com/pages/publications/84863047831
U2 - 10.1109/ICIP.2011.6116557
DO - 10.1109/ICIP.2011.6116557
M3 - Conference Paper published in a book
SN - 9781457713033
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 485
EP - 488
BT - ICIP 2011
T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011
Y2 - 11 September 2011 through 14 September 2011
ER -