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Wavelet based iterative deformable part model for pedestrian detection

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Abstract

Pedestrian detection is one of the challenging tasks in the urban traffic environments. A natural urban traffic environments include different objects like buildings, vehicles, pedestrians and so on. The conventional approach only used for a particular traffic scenario and it does not suitable for different scenarios. A novel approach is required to model these traffic scenarios. Multiresolution Morlet Decomposition Based Iterative Learning Deformable Part Model (MMD-ILDP) is proposed for improving the performance of multiresolution pedestrian detection to control the traffic in the urban area with higher accuracy. The MMD-ILDP Model uses Morlet wavelet transformation for decomposing the image into subbands with multiple resolutions. After wavelet decomposition, histogram of oriented gradients (HOG) feature pyramid is generated. Then, feature matching is performed between the pedestrian objects in the image and the feature pyramid generated with HOG and the root and part scores are computed. Finally, the root and part scores are combined to compute the final score of objects in the image. The performance measures used in evaluating the proposed algorithm are detection accuracy, time and space complexity. The simulation results show that the MMD-ILDP Model gives improved pedestrian detection in urban traffic environment where healthcare systems find more difficult to reach to the people and also reduces the time complexity on detecting the pedestrians in road traffic scenes when compared to the existing DPM and RealBoost methods.

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Authors and Affiliations

  1. Department of ECE, Coimbatore Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

    S. D. Govardhan

  2. Department of MCE, Kumaraguru College of Technology, Coimbatore, Tamilnadu, India

    A. Vasuki

Authors
  1. S. D. Govardhan
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  2. A. Vasuki
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Correspondence to S. D. Govardhan.

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Govardhan, S.D., Vasuki, A. Wavelet based iterative deformable part model for pedestrian detection. Multimed Tools Appl 79, 3667–3681 (2020). https://doi.org/10.1007/s11042-018-6435-1

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  • DOI: https://doi.org/10.1007/s11042-018-6435-1

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