Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/231960
Title: Person Re-Identification via Distance Metric Learning With Latent Variables
Authors: Chong Sun;Dong Wang;Huchuan Lu
Year: 2017
Publisher: IEEE
Abstract: In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.
URI: http://localhost/handle/Hannan/231960
volume: 26
issue: 1
More Information: 23,
34
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7600432.pdf3.54 MBAdobe PDF
Title: Person Re-Identification via Distance Metric Learning With Latent Variables
Authors: Chong Sun;Dong Wang;Huchuan Lu
Year: 2017
Publisher: IEEE
Abstract: In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.
URI: http://localhost/handle/Hannan/231960
volume: 26
issue: 1
More Information: 23,
34
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7600432.pdf3.54 MBAdobe PDF
Title: Person Re-Identification via Distance Metric Learning With Latent Variables
Authors: Chong Sun;Dong Wang;Huchuan Lu
Year: 2017
Publisher: IEEE
Abstract: In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.
URI: http://localhost/handle/Hannan/231960
volume: 26
issue: 1
More Information: 23,
34
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7600432.pdf3.54 MBAdobe PDF