Abstract
Even though there exist significant advances in recent studies, existing methods for pedestrian detection still have shown limited performances under challenging illumination conditions especially at nighttime. To address this, cross-spectral pedestrian detection methods have been presented using color and thermal, and shown substantial performance gains on the challenging circumstances. However, their paired cross-spectral settings have limited applicability in real-world scenarios. To overcome this, we propose a novel learning framework for cross-spectral pedestrian detection in an unpaired setting. Based on an assumption that features from color and thermal images share their characteristics in a common feature space to benefit their complement information, we design the separate feature embedding networks for color and thermal images followed by sharing detection networks. To further improve the cross-spectral feature representation, we apply an adversarial learning scheme to intermediate features of the color and thermal images. Experiments demonstrate the outstanding performance of the proposed method on the KAIST multi-spectral benchmark in comparison to the state-of-the-art methods.
Original language | English |
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Title of host publication | 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 1650-1654 |
Number of pages | 5 |
ISBN (Electronic) | 9781538662496 |
DOIs | |
Publication status | Published - 2019 Sep |
Event | 26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China Duration: 2019 Sep 22 → 2019 Sep 25 |
Publication series
Name | Proceedings - International Conference on Image Processing, ICIP |
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Volume | 2019-September |
ISSN (Print) | 1522-4880 |
Conference
Conference | 26th IEEE International Conference on Image Processing, ICIP 2019 |
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Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 19/9/22 → 19/9/25 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.2017K1A3A1A16066838). (Corresponding author: Kwanghoon Sohn.)
Publisher Copyright:
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Signal Processing