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An Efficient Vehicle Detection and Shadow Removal Using Gaussian Mixture Models with Blob Analysis for Machine Vision Application

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Abstract

Cities in the modern era often face a hectic challenge due to increased urbanization and industrialization. This problem arises as the number of cars on the road increases over time, and the need for traffic data becomes important. This increase is not foreseen with the creation of suitable new road sections. Accordingly, in this paper, we developed a novel algorithm to detect and count vehicles of each frame from a CCTV footage after enhancing it accordingly. Morphological filtering is used for removal of noise from each frame. The vehicles are detected using blob analysis and detected as well as counted by Gaussian mixture models. Finally, we compare our proposed method’s results against a suitable and a recent method’s YOLO (you only look once algorithm) result.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    S. Rajkumar, A. Hariharan, S. Girish & Manoj Arulmurugan

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  1. S. Rajkumar
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  2. A. Hariharan
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  3. S. Girish
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  4. Manoj Arulmurugan
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Correspondence to S. Rajkumar.

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This article is part of the topical collection “Industrial IoT and Cyber-Physical Systems” guest edited by Arun K Somani, Seeram Ramakrishnan, Anil Chaudhary and Mehul Mahrishi.

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Rajkumar, S., Hariharan, A., Girish, S. et al. An Efficient Vehicle Detection and Shadow Removal Using Gaussian Mixture Models with Blob Analysis for Machine Vision Application. SN COMPUT. SCI. 4, 451 (2023). https://doi.org/10.1007/s42979-023-01832-y

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