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Object Detection: State of the Art and Beyond

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Intelligent Scene Modeling and Human-Computer Interaction

Part of the book series: Human–Computer Interaction Series ((HCIS))

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Abstract

As one of the fundamental problems of scene understanding and modeling, object detection has attracted extensive attention in the research communities of computer vision and artificial intelligence. Recently, inspired by the success of deep learning, various deep neural network-based models have been proposed and become the de facto solution for object detection. Therefore, in this chapter, we propose to present an overview of object detection techniques in the era of deep learning. We will first formulate the problem of object detection in the framework of deep learning, and then present two mainstream architectures, i.e., the one-stage model and the two-stage model, with the widely used detectors such as Fast R-CNN, YOLO, and their variants. Lastly, we will also discuss the potential and possible improvements on current methods and outline trends for further study.

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Notes

  1. 1.

    https://wordnet.princeton.edu/.

  2. 2.

    https://pytorch.org/.

  3. 3.

    https://www.tensorflow.org/.

  4. 4.

    https://github.com/facebookarchive/caffe2.

  5. 5.

    https://keras.io/.

  6. 6.

    https://mxnet.apache.org/.

  7. 7.

    https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/MODEL_ZOO.md.

  8. 8.

    https://gluon-cv.mxnet.io/index.html.

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Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Hanhui Li, Xudong Jiang & Nadia Magnenat Thalmann

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  1. Hanhui Li
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Correspondence to Hanhui Li .

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Editors and Affiliations

  1. Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore

    Nadia Magnenat Thalmann

  2. Bournemouth University, Poole, UK

    Jian Jun Zhang

  3. Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore

    Manoj Ramanathan

  4. School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausa, Lausanne, Switzerland

    Daniel Thalmann

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Li, H., Jiang, X., Magnenat Thalmann, N. (2021). Object Detection: State of the Art and Beyond. In: Thalmann, N.M., Zhang, J.J., Ramanathan, M., Thalmann, D. (eds) Intelligent Scene Modeling and Human-Computer Interaction. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-71002-6_2

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