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Fatih Simsek

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Proceedings Article | 17 October 2023 Presentation + Paper

To what extent are color and texture effective in person re-identification

Bahadir Akdemir, İhsan Emre Üstün, Fatih Emre Şimşek, Cevahir Çiğla

Proceedings Volume 12742, 1274204 (2023) https://doi.org/10.1117/12.2683200

KEYWORDS: Color, Cameras, Color normalization, Object detection, Light sources and illumination, Surveillance, RGB color model

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In this study, we analyze the effect of color and texture information on person re-identification models. Identifying a person among different cameras involves several problems such as camera viewpoint and illumination changes, background dissimilarity, color tone as well as human pose changes. Thus, the performance of person re-identification strongly depends on the scenarios and datasets that are published for research. No matter how much convolutional neural network models (CNNs) improve, the limitations are valid since they depend on the nature of the problem. We classify person re-identification as a matching problem and focus on the effects of color and texture on the similarity scores of actual and false matches. The detailed analyses indicate that color is the most dominant cue for person re-identification and color constancy is vital to perform robust re-identification among different cameras. Besides, texture has less effect compared to color. According to these observations, we advise using color augmentations along the training stage of CNNs for re-identification problems.

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