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A New Method Used in Moving Vehicle Information Acquisition from Aerial Surveillance with a UAV

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 331))

Abstract

In this paper, a multiple moving vehicle detecting and tracking framework on aerial range data provided by the unmanned aerial vehicle (UAV) which is installed with a video camera is proposed. The system consists of two modules: moving vehicle detection, multi vehicle tracking. First of all, detect moving vehicles by clustering the singular points obtained after the motion estimation. Then, build a specific data structure to store multi-vehicle’s data and track several vehicles in the shaking video sequence from the UAV. After tracking the vehicle in the video sequence, the speed of the vehicle and record them as traffic flow information would be estimated. Finally, the method on real aerial data and the experiments are estimated and demonstrate the effectiveness of approach.

This research is supported by a grant from National High Technology Research and Development Program of China (863 Program) (2009AA11Z220).

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References

  1. Tian, Y., Lu, J., Li, Y., Wang, J.: A novel generic framework of event detection in unmanned aerial videos. In: 2010 Second International Conference on Computational Intelligence and Natural Computing Proceedings (CINC), vol. 1, pp. 357–360 (2010)

    Google Scholar 

  2. Yalcin, H., Herbert, M., Collins, R., Black, M.J.: A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video. In: Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  3. Cheng, H.-Y., Weng, C.-C., Chen, Y.-Y.: Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks. IEEE Transactions on Image Processing 21(4), 2152–2159 (2012)

    Article  MathSciNet  Google Scholar 

  4. Wang, W.-L., Li, Q.-Q., Tang, L.-L.: Algorithm of Vehicle Detection in Low Altitude Aerial Video. Journal of Wuhan University of Technology 32(10), 155–158 (2010)

    Google Scholar 

  5. Harris, C., Stephens, M.: A combined corner and edge detector (PDF). In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  6. Ding, C., He, X.: K-means Clustering via Principal Component Analysis. In: Proc.of Intel Conf. Machine Learning (ICML 2004), pp. 225–232 (July 2004)

    Google Scholar 

  7. Zhou, K., Fan, R.-X., Li, W.-X.: A MeanShift-Particle Fusion tracking algorithm based on SIFT. In: 2010 29th Chinese Control Conference (CCC), pp. 2717–2720 (2010)

    Google Scholar 

  8. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn., pp. 132–135. Pearson, Prentice Hall (2008)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Liu, F., Liu, X., Luo, P., Yang, Y., Shi, D. (2012). A New Method Used in Moving Vehicle Information Acquisition from Aerial Surveillance with a UAV. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-34595-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34594-4

  • Online ISBN: 978-3-642-34595-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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