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Detection and classification of vehicles from omnidirectional videos using multiple silhouettes

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A Correction to this article was published on 22 January 2018

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Abstract

To detect and classify vehicles in omnidirectional videos, we propose an approach based on the shape (silhouette) of the moving object obtained by background subtraction. Different from other shape-based classification techniques, we exploit the information available in multiple frames of the video. We investigated two different approaches for this purpose. One is combining silhouettes extracted from a sequence of frames to create an average silhouette, the other is making individual decisions for all frames and use consensus of these decisions. Using multiple frames eliminates most of the wrong decisions which are caused by a poorly extracted silhouette from a single video frame. The vehicle types we classify are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity and Hu moments. We applied two separate methods of classification. First one is a flowchart-based method that we developed and the second is K-nearest neighbour classification. 60% of the samples in the dataset are used for training. To ensure randomization in the experiments, threefold cross-validation is applied. The results indicate that using multiple silhouettes increases the classification performance.

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  • 22 January 2018

    An acknowledgements section was missing in this paper. It should read as follows:

Notes

  1. http://cvrg.iyte.edu.tr.

  2. http://www.gopano.com.

  3. http://www.oncamgrandeye.com/security-systems/.

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Author notes

  1. Hakki Can Karaimer

    Present address: Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Canada

Authors and Affiliations

  1. Department of Computer Engineering, Izmir Institute of Technology, 35430, Izmir, Turkey

    Hakki Can Karaimer, Ipek Baris & Yalin Bastanlar

Authors
  1. Hakki Can Karaimer
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  2. Ipek Baris
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  3. Yalin Bastanlar
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Corresponding author

Correspondence to Yalin Bastanlar.

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A correction to this article is available online at https://doi.org/10.1007/s10044-018-0684-5.

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Karaimer, H.C., Baris, I. & Bastanlar, Y. Detection and classification of vehicles from omnidirectional videos using multiple silhouettes. Pattern Anal Applic 20, 893–905 (2017). https://doi.org/10.1007/s10044-017-0593-z

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