Person Search via Deep Integrated Networks
This study proposes an integrated deep network consisting of a detection and identification module for person search. Person search is a very challenging problem because of the large appearance variation caused by occlusion, background clutter, pose variations, etc., and it is still an active resear...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/1/188 |
| _version_ | 1818844364672073728 |
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| author | Ju-Chin Chen Cheng-Feng Wu Chun-Huei Chen Cheng-Rong Lin |
| author_facet | Ju-Chin Chen Cheng-Feng Wu Chun-Huei Chen Cheng-Rong Lin |
| author_sort | Ju-Chin Chen |
| collection | DOAJ |
| description | This study proposes an integrated deep network consisting of a detection and identification module for person search. Person search is a very challenging problem because of the large appearance variation caused by occlusion, background clutter, pose variations, etc., and it is still an active research issue in the academic and industrial fields. Although various studies have been proposed, following the protocols of the person re-identification (ReID) benchmarks, most existing works take cropped pedestrian images either from manual labelling or a perfect detection assumption. However, for person search, manual processing is unavailable in practical applications, thereby causing a gap between the ReID problem setting and practical applications. One fact is also ignored: an imperfect auto-detected bounding box or misalignment is inevitable. We design herein a framework for the practical surveillance scenarios in which the scene images are captured. For person search, detection is a necessary step before ReID, and previous studies have shown that the precision of detection results has an influence on person ReID. The detection module based on the Faster R-CNN is used to detect persons in a scene image. For identifying and extracting discriminative features, a multi-class CNN network is trained with the auto-detected bounding boxes from the detection module, instead of the manually cropped data. The distance metric is then learned from the discriminative features output by the identification module. According to the experimental results of the test performed in the scene images, the multi-class CNN network for the identification module can provide a 62.7% accuracy rate, which is higher than that for the two-class CNN network. |
| first_indexed | 2024-12-19T05:12:36Z |
| format | Article |
| id | doaj.art-9a41009ad073449aa741d261a750f18f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-19T05:12:36Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9a41009ad073449aa741d261a750f18f2022-12-21T20:34:46ZengMDPI AGApplied Sciences2076-34172019-12-0110118810.3390/app10010188app10010188Person Search via Deep Integrated NetworksJu-Chin Chen0Cheng-Feng Wu1Chun-Huei Chen2Cheng-Rong Lin3Department of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung city 8078, TaiwanDepartment of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung city 8078, TaiwanDepartment of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung city 8078, TaiwanDepartment of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung city 8078, TaiwanThis study proposes an integrated deep network consisting of a detection and identification module for person search. Person search is a very challenging problem because of the large appearance variation caused by occlusion, background clutter, pose variations, etc., and it is still an active research issue in the academic and industrial fields. Although various studies have been proposed, following the protocols of the person re-identification (ReID) benchmarks, most existing works take cropped pedestrian images either from manual labelling or a perfect detection assumption. However, for person search, manual processing is unavailable in practical applications, thereby causing a gap between the ReID problem setting and practical applications. One fact is also ignored: an imperfect auto-detected bounding box or misalignment is inevitable. We design herein a framework for the practical surveillance scenarios in which the scene images are captured. For person search, detection is a necessary step before ReID, and previous studies have shown that the precision of detection results has an influence on person ReID. The detection module based on the Faster R-CNN is used to detect persons in a scene image. For identifying and extracting discriminative features, a multi-class CNN network is trained with the auto-detected bounding boxes from the detection module, instead of the manually cropped data. The distance metric is then learned from the discriminative features output by the identification module. According to the experimental results of the test performed in the scene images, the multi-class CNN network for the identification module can provide a 62.7% accuracy rate, which is higher than that for the two-class CNN network.https://www.mdpi.com/2076-3417/10/1/188person searchperson re-identificationcnn |
| spellingShingle | Ju-Chin Chen Cheng-Feng Wu Chun-Huei Chen Cheng-Rong Lin Person Search via Deep Integrated Networks Applied Sciences person search person re-identification cnn |
| title | Person Search via Deep Integrated Networks |
| title_full | Person Search via Deep Integrated Networks |
| title_fullStr | Person Search via Deep Integrated Networks |
| title_full_unstemmed | Person Search via Deep Integrated Networks |
| title_short | Person Search via Deep Integrated Networks |
| title_sort | person search via deep integrated networks |
| topic | person search person re-identification cnn |
| url | https://www.mdpi.com/2076-3417/10/1/188 |
| work_keys_str_mv | AT juchinchen personsearchviadeepintegratednetworks AT chengfengwu personsearchviadeepintegratednetworks AT chunhueichen personsearchviadeepintegratednetworks AT chengronglin personsearchviadeepintegratednetworks |