TY - GEN
T1 - Part-based Convolutional Network for Visual Tracking
AU - Zhang, Yiheng
AU - He, Hui
AU - An, Jiaoyang
AU - Ma, Bo
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Recently, Convolution Neural Networks(CNNs), which provide an valuable end-to-end image representation, have been a hot topic in visual tracking. Benefiting from the receptive field and the deep structure, CNNs can extract the deep representation of the image, which can effectively solve the target deformation in the tracking process. However, because the convolution kernel of the CNN is globally shared, it will still get disturbed features and affect the robustness of the results in background clutters, illumination variation, and so on. In this paper, we propose a novel part-based convolution network for visual tracking, which incorporates the advantages of the part-based model and the CNN for a better performance. Extensive experimental results on the OTB2013 and OTB100 tracking benchmark demonstrate that the performance of our method compares competitive with some state-of-the-art trackers.
AB - Recently, Convolution Neural Networks(CNNs), which provide an valuable end-to-end image representation, have been a hot topic in visual tracking. Benefiting from the receptive field and the deep structure, CNNs can extract the deep representation of the image, which can effectively solve the target deformation in the tracking process. However, because the convolution kernel of the CNN is globally shared, it will still get disturbed features and affect the robustness of the results in background clutters, illumination variation, and so on. In this paper, we propose a novel part-based convolution network for visual tracking, which incorporates the advantages of the part-based model and the CNN for a better performance. Extensive experimental results on the OTB2013 and OTB100 tracking benchmark demonstrate that the performance of our method compares competitive with some state-of-the-art trackers.
KW - convolution neural networks
KW - part-based
KW - visual tracking
UR - http://www.scopus.com/inward/record.url?scp=85091905943&partnerID=8YFLogxK
U2 - 10.1109/ICSIDP47821.2019.9172896
DO - 10.1109/ICSIDP47821.2019.9172896
M3 - Conference contribution
AN - SCOPUS:85091905943
T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Y2 - 11 December 2019 through 13 December 2019
ER -