Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/235535
Title: An Ensemble of Invariant Features for Person Reidentification
Authors: Young-Gun Lee;Shen-Chi Chen;Jenq-Neng Hwang;Yi-Ping Hung
Year: 2017
Publisher: IEEE
Abstract: This paper proposes an ensemble of invariant features (EIFs), which can properly handle the variations of color difference and human poses/viewpoints for matching pedestrian images observed in different cameras with nonoverlapping field of views. Our proposed method is a direct reidentification (re-id) method, which requires no prior domain learning based on prelabeled corresponding training data. The novel features consist of the holistic and region-based features. The holistic features are extracted by using a publicly available pretrained deep convolutional neural network used in generic object classification. In contrast, the region-based features are extracted based on our proposed two-way Gaussian mixture model fitting, which overcomes the self-occlusion and pose variations. To make a better generalization during recognizing identities without additional learning, the ensemble scheme aggregates all the feature distances using the similarity normalization. The proposed framework achieves robustness against partial occlusion, pose, and viewpoint changes. Moreover, the evaluation results show that our method outperforms the state-of-the-art direct re-id methods on the challenging benchmark viewpoint invariant pedestrian recognition and 3D people surveillance data sets.
URI: http://localhost/handle/Hannan/235535
volume: 27
issue: 3
More Information: 470,
483
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7779129.pdf3.94 MBAdobe PDF
Title: An Ensemble of Invariant Features for Person Reidentification
Authors: Young-Gun Lee;Shen-Chi Chen;Jenq-Neng Hwang;Yi-Ping Hung
Year: 2017
Publisher: IEEE
Abstract: This paper proposes an ensemble of invariant features (EIFs), which can properly handle the variations of color difference and human poses/viewpoints for matching pedestrian images observed in different cameras with nonoverlapping field of views. Our proposed method is a direct reidentification (re-id) method, which requires no prior domain learning based on prelabeled corresponding training data. The novel features consist of the holistic and region-based features. The holistic features are extracted by using a publicly available pretrained deep convolutional neural network used in generic object classification. In contrast, the region-based features are extracted based on our proposed two-way Gaussian mixture model fitting, which overcomes the self-occlusion and pose variations. To make a better generalization during recognizing identities without additional learning, the ensemble scheme aggregates all the feature distances using the similarity normalization. The proposed framework achieves robustness against partial occlusion, pose, and viewpoint changes. Moreover, the evaluation results show that our method outperforms the state-of-the-art direct re-id methods on the challenging benchmark viewpoint invariant pedestrian recognition and 3D people surveillance data sets.
URI: http://localhost/handle/Hannan/235535
volume: 27
issue: 3
More Information: 470,
483
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7779129.pdf3.94 MBAdobe PDF
Title: An Ensemble of Invariant Features for Person Reidentification
Authors: Young-Gun Lee;Shen-Chi Chen;Jenq-Neng Hwang;Yi-Ping Hung
Year: 2017
Publisher: IEEE
Abstract: This paper proposes an ensemble of invariant features (EIFs), which can properly handle the variations of color difference and human poses/viewpoints for matching pedestrian images observed in different cameras with nonoverlapping field of views. Our proposed method is a direct reidentification (re-id) method, which requires no prior domain learning based on prelabeled corresponding training data. The novel features consist of the holistic and region-based features. The holistic features are extracted by using a publicly available pretrained deep convolutional neural network used in generic object classification. In contrast, the region-based features are extracted based on our proposed two-way Gaussian mixture model fitting, which overcomes the self-occlusion and pose variations. To make a better generalization during recognizing identities without additional learning, the ensemble scheme aggregates all the feature distances using the similarity normalization. The proposed framework achieves robustness against partial occlusion, pose, and viewpoint changes. Moreover, the evaluation results show that our method outperforms the state-of-the-art direct re-id methods on the challenging benchmark viewpoint invariant pedestrian recognition and 3D people surveillance data sets.
URI: http://localhost/handle/Hannan/235535
volume: 27
issue: 3
More Information: 470,
483
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7779129.pdf3.94 MBAdobe PDF