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Pedestrian detection based on the privileged information

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Abstract

The pedestrian detection is always a challenging issue in the computer vision. Unlike the object recognition problem, the detection’s speed is a critical factor. In order to accelerate detection speed while maintaining competitive accuracy, in this paper we introduce a new model: twin support vector machine based on privileged information (called TSVMPI, in this paper) (Qi et al. in Neurocomputing 129:146–152, 1) to detect pedestrian. TSVMPI uses two nonparallel hyperplane classifiers to decide the label of an unknown sample and is superior to the standard SVM, especially in the linear kernel case, resulting in a significant advantage to deal with the special task. All experimental results demonstrate our strategy’s effectiveness and show that the privileged information indeed offers a significant improvement for the pedestrian detection.

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Notes

  1. http://www.princeton.edu/~rvdb/loqo/LOQO.html.

  2. \(error\,rate=\frac{\sharp\,Error\,classified}{\sharp\,Total\,samples}\).

  3. When no samples with privileged information are added to TSVMPI, TSVMPI will degenerate to TWSVM [17].

  4. These methods’ code and detection results can be found by the webpage: http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/.

  5. http://www.vision.ee.ethz.ch/~aess/dataset/.

  6. http://www.d2.mpi-inf.mpg.de/tud-brussels.

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Acknowledgements

This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 61402429, 61472390, 11271361, 11201472, 11331012), key project of National Natural Science Foundation of China (No. 71331005), and Major International (Regional) Joint Research Project (No. 71110107026).

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Authors and Affiliations

  1. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, 100190, China

    Fan Meng, Zhiquan Qi, Yingjie Tian & Lingfeng Niu

  2. Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, 100190, China

    Fan Meng, Zhiquan Qi, Yingjie Tian & Lingfeng Niu

  3. School of Economics and Management, University of Chinese Academy Sciences, Beijing, 100190, China

    Fan Meng

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  1. Fan Meng
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Correspondence to Zhiquan Qi.

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Meng, F., Qi, Z., Tian, Y. et al. Pedestrian detection based on the privileged information. Neural Comput & Applic 29, 1485–1494 (2018). https://doi.org/10.1007/s00521-016-2639-3

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