The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
A Two Stream Siamese Convolutional Neural Network for Person Re-identification
Abstract
Person re-identification is an important task in video surveillance systems. It can be formally defined as establishing the correspondence between images of a person taken from different cameras at different times. In this paper, we present a two stream convolutional neural network where each stream is a Siamese network. This architecture can learn spatial and temporal information separately. We also propose a weighted two stream training objective function which combines the Siamese cost of the spatial and temporal streams with the objective of predicting a person's identity. We evaluate our proposed method on the publicly available PRID2011 and iLIDS-VID datasets and demonstrate the efficacy of our proposed method. On average, the top rank matching accuracy is 4% higher than the accuracy achieved by the cross-view quadratic discriminant analysis used in combination with the hierarchical Gaussian descriptor (GOG+XQDA), and 5% higher than the recurrent neural network method.
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Two-Stream Convolutional Networks for Action Recognition in Videos
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DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
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Person re-identification by Local Maximal Occurrence representation and metric learning
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Signature Verification Using A "Siamese" Time Delay Neural Network
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Information-theoretic metric learning
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Deep Learning Face Representation by Joint Identification-Verification
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Large scale metric learning from equivalence constraints
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Metadata
- Authors
- Chung, D., Tahboub, K., & Delp, E. J.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1992-2000
- OpenAlex
- W2779003141
- Cited by
- 286
- References
- 34
- FWCI
- 10.52
- Type
- conference paper
- Language
- en
- Open access
- closed
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