TY - GEN
T1 - Visual tracking via multi-view semi-supervised learning
AU - Shang, Ziyu
AU - Lai, Mingzhu
AU - Ma, Bo
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/12/21
Y1 - 2018/12/21
N2 - In this paper, we present a novel visual object tracking model via multi-view semi-supervised learning. Instead of concatenating multiple views into a single view directly to adapt to conventional machine learning algorithms, the combination of views is learned by exploiting the consensus of distinct views in the entire tracking. Besides, semi-supervised learning alleviates the lack of sufficient labeled samples in the tracking task, resulting in significant improvement in generalization performance. By showing that the sample data is block-circulant, we diagonalize it with the Discrete Fourier Transform to keep the tracking at high speed. Using features extracted by the VGG-19 network and in a 1:1 ratio of the labeled samples to the unlabeled, the experiment results on the CVPR2013 Online Object Tracking Benchmark show the effectiveness of our multi-view semi-supervised tracking model.
AB - In this paper, we present a novel visual object tracking model via multi-view semi-supervised learning. Instead of concatenating multiple views into a single view directly to adapt to conventional machine learning algorithms, the combination of views is learned by exploiting the consensus of distinct views in the entire tracking. Besides, semi-supervised learning alleviates the lack of sufficient labeled samples in the tracking task, resulting in significant improvement in generalization performance. By showing that the sample data is block-circulant, we diagonalize it with the Discrete Fourier Transform to keep the tracking at high speed. Using features extracted by the VGG-19 network and in a 1:1 ratio of the labeled samples to the unlabeled, the experiment results on the CVPR2013 Online Object Tracking Benchmark show the effectiveness of our multi-view semi-supervised tracking model.
KW - Correlation Filter
KW - Multi-view Learning
KW - Semi-supervisised Learning
KW - Visual Tracking
UR - http://www.scopus.com/inward/record.url?scp=85061908749&partnerID=8YFLogxK
U2 - 10.1145/3302425.3302448
DO - 10.1145/3302425.3302448
M3 - Conference contribution
AN - SCOPUS:85061908749
T3 - ACM International Conference Proceeding Series
BT - ACAI 2018 Conference Proceeding - 2018 International Conference on Algorithms, Computing and Artificial Intelligence
PB - Association for Computing Machinery
T2 - 2018 International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2018
Y2 - 21 December 2018 through 23 December 2018
ER -