TY - GEN
T1 - Evaluation of deep convolutional neural network-based representations for cross dataset person re-identification
AU - Ulu, Alper
AU - Ekenel, Hazim Kemal
N1 - Publisher Copyright:
© 2017 by SCITEPRESS - Science and Technology Publications, Lda.
PY - 2017
Y1 - 2017
N2 - Video surveillance systems have great importance to ensure public safety. Today, these kind of systems not only capture and distribute video but also have various smart applications. Person re-identification is one of the most important of these applications. In this work, we have exploited deep convolutional neural networkbased representations for cross dataset person re-identification problem. We have selected well-known deep convolutional neural network models, namely, AlexNet, VGG-16, and GoogLeNet, and fine-tuned them with the largest publicly available person re-identification datasets. We have employed cosine similarity metric to calculate the similarity between extracted features. CUHK03 and Market-1501 datasets were used as the training sets and the proposed method has been tested on the VIPeR dataset. Superior results have been obtained with the proposed method compared to the state-of-the-art methods in the field.
AB - Video surveillance systems have great importance to ensure public safety. Today, these kind of systems not only capture and distribute video but also have various smart applications. Person re-identification is one of the most important of these applications. In this work, we have exploited deep convolutional neural networkbased representations for cross dataset person re-identification problem. We have selected well-known deep convolutional neural network models, namely, AlexNet, VGG-16, and GoogLeNet, and fine-tuned them with the largest publicly available person re-identification datasets. We have employed cosine similarity metric to calculate the similarity between extracted features. CUHK03 and Market-1501 datasets were used as the training sets and the proposed method has been tested on the VIPeR dataset. Superior results have been obtained with the proposed method compared to the state-of-the-art methods in the field.
KW - Convolutional Neural Networks
KW - Deep Learning
KW - Person Re-identification
UR - http://www.scopus.com/inward/record.url?scp=85047874229&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047874229&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85047874229
T3 - VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
SP - 571
EP - 578
BT - VISAPP
A2 - Tremeau, Alain
A2 - Braz, Jose
A2 - Imai, Francisco
PB - SciTePress
T2 - 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017
Y2 - 27 February 2017 through 1 March 2017
ER -