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Deep Learning in Object Detection and Recognition pp 19–57Cite as

Deep Learning in Object Detection

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Abstract

Object detection is an important research area in image processing and computer vision. The performance of object detection has significantly improved through applying deep learning technology. Among these methods, convolutional neural network (CNN)-based methods are most frequently used. CNN methods mainly include two classes: two-stage methods and one-stage methods. This chapter firstly introduces some typical CNN-based architectures in details. After that, pedestrian detection, as a classical subset of object detection, is further introduced. According to whether CNN is used or not, pedestrian detection can be divided into two types: handcrafted feature-based methods and CNN-based methods. Among these methods, NNNF (non-neighboring and neighboring features) inspired by pedestrian attributes (i.e., appearance constancy and shape symmetry) and MCF based on handcrafted channels and each layer of CNN are specifically illustrated. Finally, some challenges of object detection (i.e., scale variation, occlusion, and deformation) will be discussed.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Yanwei Pang & Jiale Cao

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  1. Yanwei Pang
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  2. Jiale Cao
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Correspondence to Yanwei Pang .

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Editors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Xiaoyue Jiang  & Xiaoyi Feng  & 

  2. Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Oulu, Finland

    Abdenour Hadid

  3. School of Electrical and Information Engineering, Tianjin University, Tianjin, Tianjin, China

    Yanwei Pang

  4. École de technologie supérieure, University of Québec, Montréal, QC, Canada

    Eric Granger

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Pang, Y., Cao, J. (2019). Deep Learning in Object Detection. In: Jiang, X., Hadid, A., Pang, Y., Granger, E., Feng, X. (eds) Deep Learning in Object Detection and Recognition. Springer, Singapore. https://doi.org/10.1007/978-981-10-5152-4_2

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  • DOI: https://doi.org/10.1007/978-981-10-5152-4_2

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