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Color Inference from Semantic Labeling for Person Search in Videos

  • Jules SimonEmail author
  • Guillaume-Alexandre BilodeauEmail author
  • David Steele
  • Harshad Mahadik
Conference paper
  • 130 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12131)

Abstract

We propose an explainable model for classifying the color of pixels in images. We propose a method based on binary search trees and a large peer-labeled color name dataset, allowing us to synthesize the average human perception of colors. We test our method on the application of Person Search. In this context, persons are described from their semantic parts, such as hat, shirt, ... and person search consists in looking for people based on these descriptions. We label segments of pedestrians with their associated colors and evaluate our solution on datasets such as PCN and Colorful-Fashion. We show a precision as high as 83% as well as the model ability to generalize to multiple domains with no retraining.

Keywords

Color classification Person search Semantic color labeling 

Notes

Acknowlegments

We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), [CRDPJ 528786 - 18], and the support of Arcturus network.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.LITIV Lab.Polytechnique MontréalMontréalCanada
  2. 2.Arcturus NetworksEtobicokeCanada

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