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Performance Enhancements for the Detection of Rectangular Traffic Signs

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Advanced Microsystems for Automotive Applications 2016

Part of the book series: Lecture Notes in Mobility ((LNMOB))

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Abstract

Most countries around the world present regulation and rules, applying on public roads, by putting up traffic signs. Therefore it is useful for driver assistance systems and important for autonomous vehicles to understand the meaning and consequences of those signs. One class of traffic signs that present important information is speed limit signs, which underlie strict norms. In this paper, we will introduce performance enhancing methods for the detection of rectangular traffic signs on the example of speed limit signs in the United States of America (USA). We will show that with a small and acceptable loss of accuracy the number of calculations needed and their complexity can be greatly reduced. Due to that, the energy consumption of the embedded hardware and the processing time per frame are reduced.

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References

  1. Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks. In: The 2011 International joint conference on neural networks (IJCNN), IEEE, pp 2809–2813

    Google Scholar 

  2. De la Escalera A, Armingol JM, Mata M (2003) Traffic sign recognition and analysis for intelligent vehicles. Image Vis Comput 21(3):247–258

    Article  Google Scholar 

  3. Barnes N, Zelinsky A, Fletcher LS (2008) Real-time speed sign detection using the radial symmetry detector. IEEE Trans Intell Transp Syst 9(2):322–332

    Article  Google Scholar 

  4. De La Escalera A et al (1997) Road traffic sign detection and classification. IEEE Trans Ind Electron 44(6):848–859

    Article  Google Scholar 

  5. Loy G, Barnes N (2004) Fast shape-based road sign detection for a driver assistance system. In: IROS: 2004 IEEE/RSJ International conference on intelligent robots and systems, pp 70–75

    Google Scholar 

  6. Eickeler S, Valdenegro M (2016) Future computer vision algorithms for traffic sign recognition systems. In: Schulze T, Müller B, Meyer G (eds) Advanced microsystems for automotive applications 2015—smart systems for green and automated driving. Springer International Publishing, Berlin, pp 69–77

    Google Scholar 

  7. Crow FC (1984) Summed-area tables for texture mapping. ACM SIGGRAPH Comput Graph 18(3):207–212

    Article  Google Scholar 

  8. Møgelmose A, Trivedi MM, Moeslund TB (2012) Vision based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans Intell Transp Syst

    Google Scholar 

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Correspondence to Lukas Pink .

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© 2016 Springer International Publishing AG

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Pink, L., Eickeler, S. (2016). Performance Enhancements for the Detection of Rectangular Traffic Signs. In: Schulze, T., Müller, B., Meyer, G. (eds) Advanced Microsystems for Automotive Applications 2016. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-319-44766-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-44766-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44765-0

  • Online ISBN: 978-3-319-44766-7

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