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Enhanced Aggregated Channel Features Detector for Pedestrian Detection Using Parameter Optimisation and Deep Features

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Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 841))

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Abstract

Aggregated Channel Features (ACF) proposed by Dollar et al. provide strong framework for pedestrian detection. Many variants of ACF detector achieved state of the art result using deep features along with aggregated channel features. In this paper we propose a hybrid method for pedestrian detection using a parameter optimized variant of ACF detector with decorrelated channels as region proposer followed by a deep CNN for feature extraction. Our proposed method effectively handles the issues of false positives and detection of small instances of pedestrians. The proposed detector gives the best result among the different variants of the ACF detectors in Caltech dataset with the best localization and is second to the best performing detector available till date.

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Acknowledgements

We gratefully acknowledge for the research fellowship (3501/(NET-DEC.2014)) provided by the University Grants Commission (UGC) Govt. of India.

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Authors and Affiliations

  1. College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India

    Blossom Treesa Bastian & C. V. Jiji

Authors
  1. Blossom Treesa Bastian
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  2. C. V. Jiji
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Correspondence to Blossom Treesa Bastian .

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Editors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Renu Rameshan

  2. Indraprastha Institute of Information Technology, New Delhi, India

    Chetan Arora

  3. Indian Institute of Technology, New Delhi, India

    Sumantra Dutta Roy

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Bastian, B.T., Jiji, C.V. (2018). Enhanced Aggregated Channel Features Detector for Pedestrian Detection Using Parameter Optimisation and Deep Features. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_12

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  • DOI: https://doi.org/10.1007/978-981-13-0020-2_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0019-6

  • Online ISBN: 978-981-13-0020-2

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