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Robust Vehicle Blob Tracking with Split/Merge Handling

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4122))

Abstract

Evaluation results of a vehicle tracking system on a given set of evaluation videos of a street surveillance system are presented. The method largely depends on detection of motion by comparison with a learned background model. Several difficulties of the task are overcome by the use of general constrains of scene, camera and vehicle models. An analysis of results is also presented.

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References

  1. Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Boonstra, M., Korzhova, V.: Performance Evaluation Protocal for Face, Person and Vehicle Detection & Tracking in Video Analysis and Centent Extraction (VACE-II). In: CLEAR - Classification of Events, Activities and Relationships (2006), http://www.nist.gov/speech/tests/clear/2006/CLEAR06-R106-EvalDiscDoc/DataandInformation/ClearEval_Protocol_v5.pdf

  2. Li, L., Huang, W., Gu, I.Y.H., Tian, Q.: Foreground Object Detection from Videos Containing Complex Background. ACM MM (2003)

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  4. Lv, F., Zhao, T., Nevatia, R.: Self-Calibration of a Camera from Video of a Walking Human. In: 16th International Conference on Pattern Recognition (ICPR), Quebec, Canada (2002)

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Rainer Stiefelhagen John Garofolo

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© 2007 Springer Berlin Heidelberg

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Song, X., Nevatia, R. (2007). Robust Vehicle Blob Tracking with Split/Merge Handling. In: Stiefelhagen, R., Garofolo, J. (eds) Multimodal Technologies for Perception of Humans. CLEAR 2006. Lecture Notes in Computer Science, vol 4122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69568-4_18

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  • DOI: https://doi.org/10.1007/978-3-540-69568-4_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69567-7

  • Online ISBN: 978-3-540-69568-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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