Title | Improving person re-identification performance by customized dataset and person detection |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Groot, HGJ, Bondarau, E, de With, PHN |
Conference Name | Image Processing: Algorithms and Systems XVII, IPAS2019 |
Date Published | 02/2019 |
Abstract | For person re-identification (re-ID), nearly all person re-ID algorithms use public person re-ID datasets, where these datasets all consist of predefined image crops containing a single person. Unfortunately, these image crops are not optimal for video analysis, so that the person detection becomes suboptimal and person re-ID obtains a lower performance score. In this work, several techniques are presented that customize the person images of a popular public person re-ID dataset. These techniques consist of customization algorithms based on postprocessing the person-detection bounding boxes using the original frames, resulting in several customized datasets to better facilitate person re-identification. We have evaluated five different ways for customization, based on widening the image crops, various aspect ratios and resolutions, and person instance segmentation. We have obtained a significant increase in performance with widened image crops, yielding a convincing performance increase of nearly 3{%} in the resulting Rank-1 score. Furthermore, when the applied random-cropping process is further optimized to this customization technique, an increase of even more than 4{%} is obtained. Both performance gains are a strong indication that any future person re-ID system may benefit from customizations based on the original video frames or from specializing the person detector. |
URL | http://www.imaging.org/site/IST/IST/Conferences/EI/EI_2019/Conference/C_IPAS.aspx |
DOI | 10.2352/ISSN.2470-1173.2019.11.IPAS-268 |
Improving person re-identification performance by customized dataset and person detection
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