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Occlusion Detection Based on Fractal Texture Analysis in Surveillance Videos Using Tree-Based Classifiers

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Security in Computing and Communications (SSCC 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 536))

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Abstract

Occlusion detection in video has been an active research for decades. This interest is motivated by numerous applications, such as visual surveillance, human-computer interaction, and sports event analysis. In this paper, an occlusion detection approach based on fractal texture analysis is proposed. Texture features are extracted from the segmented images using Segmentation-based Fractal Texture Analysis (SFTA) algorithm. The experiments are carried out using a PNNL-Parking-Lot dataset and the various tree-based classifiers such as random forest, random tree, decision tree (J48), and REP tree are used for classification. In the experiment results, random forest classifier showed the best performance with an overall accuracy rate of 98.3 % for SET-1, 98.2 % for SET-2, and 83.7 % for SET-3, which outperforms other algorithms.

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Authors and Affiliations

  1. Speech and Vision Laboratory, Department of Computer Science and Engineering, Annamalai University, Annamalainagar, Tamilnadu, India

    J. Arunnehru, M. Kalaiselvi Geetha & T. Nanthini

Authors
  1. J. Arunnehru
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  2. M. Kalaiselvi Geetha
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  3. T. Nanthini
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Corresponding author

Correspondence to J. Arunnehru .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Jemal H. Abawajy

  2. IBM Research-India, New Delhi, India

    Sougata Mukherjea

  3. Indian Institute of Information Technology and Management, Kerala, India

    Sabu M. Thampi

  4. University of Murcia, Espinardo, Murcia, Spain

    Antonio Ruiz-Martínez

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Arunnehru, J., Kalaiselvi Geetha, M., Nanthini, T. (2015). Occlusion Detection Based on Fractal Texture Analysis in Surveillance Videos Using Tree-Based Classifiers. In: Abawajy, J., Mukherjea, S., Thampi, S., Ruiz-Martínez, A. (eds) Security in Computing and Communications. SSCC 2015. Communications in Computer and Information Science, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-22915-7_29

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  • DOI: https://doi.org/10.1007/978-3-319-22915-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22914-0

  • Online ISBN: 978-3-319-22915-7

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