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A Sparse-Dense HOG Window Sampling Technique for Fast Pedestrian Detection in Aerial Images

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Innovations in Electrical and Electronic Engineering (ICEEE 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 893))

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Abstract

Pedestrian detection from Unmanned Aerial Vehicle (UAV) has been an important part of surveillance systems. A Two-stage (Sparse-Dense) sliding window technique has been proposed to increase the speed of pedestrian detection using HOG-SVM classifier. Standard techniques follow a sliding window approach with a fixed sliding strides over a multi-resolution image pyramid for detection. The presented technique breaks down the detection task into sparse sampling and dense sampling stages where the first one is region proposal step and second stage scans only the proposed regions for objects. Sparse sampling stage is working as weak classifier whereas the dense sampling stage works as strong classifier for an image patch. Average pedestrian detection speed using the proposed technique gave improvement from 1.95 fps to 15.36 fps for input images of dimension [640, 360] on a system with 3.2 GHz CPU. UAV123 [1] dataset has been chosen to train the classifier. For detection, Average Center Prediction Error has been taken to quantify detection performance with increased speed.

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Acknowledgement

We kindly acknowledge IMPRINT I project, MHRD, Govt. of India for supporting with resources from the project “Decentralized target tracking using swarm of aerial robots”.

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Authors and Affiliations

  1. Indian Institute of Technology, Kharagpur, India

    Ranjeet Kumar & Alok Kanti Deb

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  1. Ranjeet Kumar
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  2. Alok Kanti Deb
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Correspondence to Ranjeet Kumar .

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Editors and Affiliations

  1. School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia

    Saad Mekhilef

  2. Office of the International Relations, Bharath Institute of Higher Education and Research, Chennai, India

    Rabindra Nath Shaw

  3. Department of Management and Innovation Systems, University of Salerno, Fisciano, Salerno, Italy

    Pierluigi Siano

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Kumar, R., Deb, A.K. (2022). A Sparse-Dense HOG Window Sampling Technique for Fast Pedestrian Detection in Aerial Images. In: Mekhilef, S., Shaw, R.N., Siano, P. (eds) Innovations in Electrical and Electronic Engineering. ICEEE 2022. Lecture Notes in Electrical Engineering, vol 893. Springer, Singapore. https://doi.org/10.1007/978-981-19-1742-4_37

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  • DOI: https://doi.org/10.1007/978-981-19-1742-4_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1741-7

  • Online ISBN: 978-981-19-1742-4

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