TY - GEN
T1 - A novel semi-supervised detection approach with weak annotation
AU - Tokuda, Eric K.
AU - Ferreira, Gabriel B.A.
AU - Silva, Claudio
AU - Cesar, Roberto M.
N1 - Funding Information:
The authors would like to thank FAPESP grants #2015/22308-2, #15/03475-5, #16/12077-6, #14/24918-0, CNPq, CAPES and NAP eScience - PRP - USP.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/21
Y1 - 2018/9/21
N2 - In this work we propose a semi-supervised learning approach for object detection where we use detections from a preexisting detector to train a new detector. We differ from previous works by coming up with a relative quality metric which involves simpler labeling and by proposing a full framework of automatic generation of improved detectors. To validate our method, we collected a comprehensive dataset of more than two thousand hours of streaming from public traffic cameras that contemplates variations in time, location and weather. We used these data to generate and assess with weak labeling a car detector that outperforms popular detectors on hard situations such as rainy weather and low resolution images. Experimental results are reported, thus corroborating the relevance of the proposed approach.
AB - In this work we propose a semi-supervised learning approach for object detection where we use detections from a preexisting detector to train a new detector. We differ from previous works by coming up with a relative quality metric which involves simpler labeling and by proposing a full framework of automatic generation of improved detectors. To validate our method, we collected a comprehensive dataset of more than two thousand hours of streaming from public traffic cameras that contemplates variations in time, location and weather. We used these data to generate and assess with weak labeling a car detector that outperforms popular detectors on hard situations such as rainy weather and low resolution images. Experimental results are reported, thus corroborating the relevance of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=85055537347&partnerID=8YFLogxK
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U2 - 10.1109/SSIAI.2018.8470307
DO - 10.1109/SSIAI.2018.8470307
M3 - Conference contribution
AN - SCOPUS:85055537347
T3 - Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
SP - 129
EP - 132
BT - 2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018
Y2 - 8 April 2018 through 10 April 2018
ER -