IEICE TRANSACTIONS on Information
A Fast Non-Overlapping Multi-Camera People Re-Identification Algorithm and Tracking Based on Visual Channel Model
Summary :
In this paper, a nonoverlapping multi-camera and people re-identification algorithm is proposed. It applies inflated major color features for re-identification to reduce computation time. The inflated major color features can dramatically improve efficiency while retaining high accuracy of object re-identification. The proposed method is evaluated over a wide range of experimental databases. The accuracy attains upwards of 40.7% in Rank 1 and 84% in Rank 10 on average, while it obtains three to 15 times faster than algorithms reported in the literature. The proposed algorithm has been implemented on a SOC-FPGA platform to reach 50 FPS with 1280×720 HD resolution and 25 FPS with 1920×1080 FHD resolution for real-time processing. The results show a performance improvement and reduction in computation complexity, which is especially ideal for embedded platform.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.7 pp.1342-1348
- Publication Date
- 2019/07/01
- Publicized
- 2019/04/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7348
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Chi-Chia SUN
Department of Electrical Engineering National Formosa University
Ming-Hwa SHEU
Department of Electronic Engineering National Yunlin University of Science and Technology
Jui-Yang CHI
Department of Electronic Engineering National Yunlin University of Science and Technology
Yan-Kai HUANG
Department of Electronic Engineering National Yunlin University of Science and Technology
Keyword
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Chi-Chia SUN, Ming-Hwa SHEU, Jui-Yang CHI, Yan-Kai HUANG, "A Fast Non-Overlapping Multi-Camera People Re-Identification Algorithm and Tracking Based on Visual Channel Model" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 7, pp. 1342-1348, July 2019, doi: 10.1587/transinf.2018EDP7348.
Abstract: In this paper, a nonoverlapping multi-camera and people re-identification algorithm is proposed. It applies inflated major color features for re-identification to reduce computation time. The inflated major color features can dramatically improve efficiency while retaining high accuracy of object re-identification. The proposed method is evaluated over a wide range of experimental databases. The accuracy attains upwards of 40.7% in Rank 1 and 84% in Rank 10 on average, while it obtains three to 15 times faster than algorithms reported in the literature. The proposed algorithm has been implemented on a SOC-FPGA platform to reach 50 FPS with 1280×720 HD resolution and 25 FPS with 1920×1080 FHD resolution for real-time processing. The results show a performance improvement and reduction in computation complexity, which is especially ideal for embedded platform.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7348/_p
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@ARTICLE{e102-d_7_1342,
author={Chi-Chia SUN, Ming-Hwa SHEU, Jui-Yang CHI, Yan-Kai HUANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Fast Non-Overlapping Multi-Camera People Re-Identification Algorithm and Tracking Based on Visual Channel Model},
year={2019},
volume={E102-D},
number={7},
pages={1342-1348},
abstract={In this paper, a nonoverlapping multi-camera and people re-identification algorithm is proposed. It applies inflated major color features for re-identification to reduce computation time. The inflated major color features can dramatically improve efficiency while retaining high accuracy of object re-identification. The proposed method is evaluated over a wide range of experimental databases. The accuracy attains upwards of 40.7% in Rank 1 and 84% in Rank 10 on average, while it obtains three to 15 times faster than algorithms reported in the literature. The proposed algorithm has been implemented on a SOC-FPGA platform to reach 50 FPS with 1280×720 HD resolution and 25 FPS with 1920×1080 FHD resolution for real-time processing. The results show a performance improvement and reduction in computation complexity, which is especially ideal for embedded platform.},
keywords={},
doi={10.1587/transinf.2018EDP7348},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - A Fast Non-Overlapping Multi-Camera People Re-Identification Algorithm and Tracking Based on Visual Channel Model
T2 - IEICE TRANSACTIONS on Information
SP - 1342
EP - 1348
AU - Chi-Chia SUN
AU - Ming-Hwa SHEU
AU - Jui-Yang CHI
AU - Yan-Kai HUANG
PY - 2019
DO - 10.1587/transinf.2018EDP7348
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2019
AB - In this paper, a nonoverlapping multi-camera and people re-identification algorithm is proposed. It applies inflated major color features for re-identification to reduce computation time. The inflated major color features can dramatically improve efficiency while retaining high accuracy of object re-identification. The proposed method is evaluated over a wide range of experimental databases. The accuracy attains upwards of 40.7% in Rank 1 and 84% in Rank 10 on average, while it obtains three to 15 times faster than algorithms reported in the literature. The proposed algorithm has been implemented on a SOC-FPGA platform to reach 50 FPS with 1280×720 HD resolution and 25 FPS with 1920×1080 FHD resolution for real-time processing. The results show a performance improvement and reduction in computation complexity, which is especially ideal for embedded platform.
ER -
English
