Transfer learning based approach for semantic person retrieval
Transfer learning based approach for semantic person retrieval
Many algorithms for semantic person retrieval suffer from a lack of training data often due to the difficulties in constructing a large dataset. We therefore propose a transfer learning based approach for semantic person identification and semantic person search. We apply the fine-tuned Mask R-CNN and DenseNet-161 for detection and attribute classification. The networks were pre-trained on the MS
COCO and ILSVRC 2012 datasets. Our proposed approach achieves the highest recognition rate at each rank of CMC curve for semantic person identification and the highest average localization precision for semantic person search on our validation dataset.
Yaguchi, Takuya
4c2dcd5f-d8e1-4e12-847c-7eab499c6f8b
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Yaguchi, Takuya
4c2dcd5f-d8e1-4e12-847c-7eab499c6f8b
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Yaguchi, Takuya and Nixon, Mark
(2018)
Transfer learning based approach for semantic person retrieval.
In 15th IEEE International Conference on Advanced Video and Signal-based Surveillance.
IEEE..
(In Press)
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Conference or Workshop Item
(Paper)
Abstract
Many algorithms for semantic person retrieval suffer from a lack of training data often due to the difficulties in constructing a large dataset. We therefore propose a transfer learning based approach for semantic person identification and semantic person search. We apply the fine-tuned Mask R-CNN and DenseNet-161 for detection and attribute classification. The networks were pre-trained on the MS
COCO and ILSVRC 2012 datasets. Our proposed approach achieves the highest recognition rate at each rank of CMC curve for semantic person identification and the highest average localization precision for semantic person search on our validation dataset.
More information
Accepted/In Press date: November 2018
Venue - Dates:
15th IEEE International Conference on Advanced Video and Signal-based Surveillance: AVSS 2018, , Aukland, New Zealand, 2018-11-27 - 2018-11-30
Identifiers
Local EPrints ID: 426371
URI: http://eprints.soton.ac.uk/id/eprint/426371
PURE UUID: 274a0333-2403-4990-a395-daced75ad1d6
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Date deposited: 26 Nov 2018 17:30
Last modified: 16 Mar 2024 07:19
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Contributors
Author:
Takuya Yaguchi
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