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Shahbazi, M.; Sattari, M.; Homayouni, S.; Saadatseresht, M. (2012)
Publisher: Copernicus Publications
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Recent advances in positioning techniques have made it possible to develop Mobile Mapping Systems (MMS) for detection and 3D localization of various objects from a moving platform. On the other hand, automatic traffic sign recognition from an equipped mobile platform has recently been a challenging issue for both intelligent transportation and municipal database collection. However, there are several inevitable problems coherent to all the recognition methods completely relying on passive chromatic or grayscale images. This paper presents the implementation and evaluation of an operational MMS. Being distinct from the others, the developed MMS comprises one range camera based on Photonic Mixer Device (PMD) technology and one standard 2D digital camera. The system benefits from certain algorithms to detect, recognize and localize the traffic signs by fusing the shape, color and object information from both range and intensity images. As the calibrating stage, a self-calibration method based on integrated bundle adjustment via joint setup with the digital camera is applied in this study for PMD camera calibration. As the result, an improvement of 83 % in RMS of range error and 72 % in RMS of coordinates residuals for PMD camera, over that achieved with basic calibration is realized in independent accuracy assessments. Furthermore, conventional photogrammetric techniques based on controlled network adjustment are utilized for platform calibration. Likewise, the well-known Extended Kalman Filtering (EKF) is applied to integrate the navigation sensors, namely GPS and INS. The overall acquisition system along with the proposed techniques leads to 90 % true positive recognition and the average of 12 centimetres 3D positioning accuracy.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Fraser, C.S., 1997. Digital camera self-calibration. ISPRS J. Photogramm., 52, pp. 149-159.
    • Grewal, M.S., Weill, L.R., Andrews A.P, 2007. Global Positioning Systems, Inertial Navigation, and Integration. John Wiley & Sons Publication.
    • Laurent, S., Jerome, N., Thomas, G., 2010. Global environment interpretation from a new mobile mapping system. In: Proceedings of IEEE Intelligent Vehicles Symposium , San Diego, CA, USA, pp. 941-948.
    • Moller, T., Kraft, H., Frey, J., 2005. Albrecht, M., Lange R. Robust 3D Measurement with PMD Sensors. In: Proceedings of the First Range Imaging Research Day, ETH Zurich, Switzerland.
    • Seeber, G., 2003. Satellite Geodesy: 2nd Completely Revised and Extended Edition. Walter de Gruyter, Berlin and New York.
    • Shahbazi, M., Homayouni, S., Saadatseresht, M., Sattari, M., 2011a. Range Camera Self-Calibration Based on Integrated Bundle Adjustment via Joint Setup with a 2D Digital Camera. Sensors, 11(9), pp. 8721-8740.
    • Shahbazi, M., Sattari, M., Homayouni, S., Saadatseresht, M., 2011b. Object-based traffic sign detection and recognition from integrated range and intensity images. In: Proceedings of IASTED SIP and ACTAPRESS, Texas, USA, Vol. 13.
    • Van den Bergh, M., Van Gool, L., 2011. Combining RGB and ToF cameras for real-time 3D hand gesture interaction. In: Proceedings of the IEEE Workshop on Applications of Computer Vision, Kona, HI, USA, pp. 66-72.
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