Learning to tell brake lights with convolutional features

Guangyu Zhong, Yi Hsuan Tsai, Yi Ting Chen, Xue Mei, Danil Prokhorov, Michael James, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper, we present a learning-based brake light classification algorithm for intelligent driver-Assistance systems. State-of-The-Art approaches apply different image processing techniques with hand-crafted features to determine whether brake lights are on or off. In contrast, we learn a brake light classifier based on discriminative color descriptors and convolutional features fine-Tuned for traffic scenes. We show how brake light regions can be segmented and classified in one framework. Numerous experimental results show that the proposed algorithm performs well against state-of-The-Art alternatives in real-world scenes.

Original languageEnglish
Title of host publication2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1558-1563
Number of pages6
ISBN (Electronic)9781509018895
DOIs
Publication statusPublished - 2016 Dec 22
Event19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016 - Rio de Janeiro, Brazil
Duration: 2016 Nov 12016 Nov 4

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Conference

Conference19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016
CountryBrazil
CityRio de Janeiro
Period16/11/116/11/4

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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  • Cite this

    Zhong, G., Tsai, Y. H., Chen, Y. T., Mei, X., Prokhorov, D., James, M., & Yang, M. H. (2016). Learning to tell brake lights with convolutional features. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016 (pp. 1558-1563). [7795765] (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2016.7795765