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Particle Filter for Trajectories of Movers from Laser Scanned Dataset

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Pattern Recognition and Artificial Intelligence (MedPRAI 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1144))

Abstract

Laser scanner takes away the problem of private life conservation as it does not record real world videos except scanned data points. So it shows many benefits over the use of video camera. This paper portrays an approach to detect and track movers from laser scanned datasets. Laser scanned data points from each scan are deemed as a video frame. Blobs are extracted from each frame. Support vector machine (SVM) and Hungarian method along with particle filter are used to get trajectories of movers. Experimental results on the identical laser scanned dataset demonstrate that the approach of SVM with Hungarian method using particle filter outperforms both the threshold based approach with Hungarian method using Kalman filter and the approach of SVM with Hungarian method using Kalman filter.

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Authors and Affiliations

  1. University of Hail, Hail, Kingdom of Saudi Arabia

    Md. Haidar Sharif

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  1. Md. Haidar Sharif
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Correspondence to Md. Haidar Sharif .

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  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    Akhtar Jamil

  3. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

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Sharif, M.H. (2020). Particle Filter for Trajectories of Movers from Laser Scanned Dataset. In: Djeddi, C., Jamil, A., Siddiqi, I. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2019. Communications in Computer and Information Science, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-030-37548-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-37548-5_11

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