Abstract
Person re-identification is a key challenge for surveillance across multiple sensors. Prompted by the advent of powerful deep learning models for visual recognition, and inexpensive RGBD cameras and sensor-rich mobile robotic platforms, e.g. self-driving vehicles, we investigate the relatively unexplored problem of cross-modal re-identification of persons between RGB (color) and depth images. The considerable divergence in data distributions across different sensor modalities introduces additional challenges to the typical difficulties like distinct viewpoints, occlusions, and pose and illumination variation. While some work has investigated re-identification across RGB and infrared, we take inspiration from successes in transfer learning from RGB to depth in object detection tasks. Our main contribution is a novel cross-modal distillation network for robust person re-identification, which learns a shared feature representation space of person's appearance in both RGB and depth images. The proposed network was compared to conventional and deep learning approaches proposed for other cross-domain re-identification tasks. Results obtained on the public BIWI and RobotPKU datasets indicate that the proposed method can significantly outperform the state-of-the-art approaches by up to 10.5% mAp, demonstrating the benefit of the proposed distillation paradigm.
Original language | English |
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Title of host publication | Proceedings of the 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2019) |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Number of pages | 8 |
ISBN (Electronic) | 978-1-7281-0990-9 |
DOIs | |
Publication status | Published - 2019 |
Event | 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019 - Taipei, Taiwan Duration: 18 Sept 2019 → 21 Sept 2019 |
Conference
Conference | 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019 |
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Country/Territory | Taiwan |
City | Taipei |
Period | 18/09/19 → 21/09/19 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.