TY - GEN
T1 - ORB-based Template Matching Through Convolutional Features Map
AU - Zhou, Dong
AU - Tian, Yingxin
AU - Li, Xiang
AU - Wu, Jiefei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Template matching is an important part of computer vision, but most of common methods do not work well in some cases, such as complicated background clutter, deformation and partial occlusion. Therefore, we present a novel method for image matching, which is useful, robust and fast. Its essence is the ORB-based Convolutional Features Map (CFM), which is used to measure the similarity between template and target image. We study its properties and apply it to a real-world dataset in complex environment. The result of experiments demonstrates that our algorithm outperforms other commonly used algorithm.
AB - Template matching is an important part of computer vision, but most of common methods do not work well in some cases, such as complicated background clutter, deformation and partial occlusion. Therefore, we present a novel method for image matching, which is useful, robust and fast. Its essence is the ORB-based Convolutional Features Map (CFM), which is used to measure the similarity between template and target image. We study its properties and apply it to a real-world dataset in complex environment. The result of experiments demonstrates that our algorithm outperforms other commonly used algorithm.
KW - ORB-based convolutional feature map
KW - Object Tracking
KW - template matching
UR - https://www.scopus.com/pages/publications/85080032184
U2 - 10.1109/CAC48633.2019.8997228
DO - 10.1109/CAC48633.2019.8997228
M3 - 会议稿件
AN - SCOPUS:85080032184
T3 - Proceedings - 2019 Chinese Automation Congress, CAC 2019
SP - 4695
EP - 4699
BT - Proceedings - 2019 Chinese Automation Congress, CAC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Chinese Automation Congress, CAC 2019
Y2 - 22 November 2019 through 24 November 2019
ER -