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Adaptive pedestrian detection by predicting classifier

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Abstract

Generally the performance of a pedestrian detector will decrease rapidly, when it is trained on a fixed training set but applied to specific scenes. The reason is that in the training set only a few samples are useful for the specific scenes while other samples may disturb the accurate detections. Traditional methods solve this problem by transfer learning which suffer the problem of keeping source samples or artificially labeling a few samples in the detection phase. In this paper, we propose a new method to bypass these defects by predicting pedestrian classifier for each sample in the detection phase. A classifier regression model is trained in the source domain in which each sample has a proprietary classifier. In the detection phase, a pedestrian classifier is predicted for each candidate window in an image. Thus, for the samples in the target domain, the pedestrian classifiers are different. Our main contributions are: (1) a new adaptive detector without keeping source samples or labeling a few new target samples; (2) a new dimensionality reduction method for classifier vector which simultaneously ensures the performance of both reconstruction and classification; (3) a two-stage regression neural model which can handle the high-dimensional regression problem effectively. Experiments prove that our method can achieve the state-of-the-art results on two pedestrian datasets.

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Notes

  1. Download link: http://vision.ucsd.edu/~pdollar/toolbox/.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61375038) and Applied Basic Research Programs of Sichuan Science and Technology Department (2016JY0088).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Center for Robotics, Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Song Tang, Mao Ye, Pei Xu & Xudong Li

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  1. Song Tang
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  2. Mao Ye
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  3. Pei Xu
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  4. Xudong Li
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Correspondence to Mao Ye.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, Adaptive Pedestrian Detection by Predicting Classifier.

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Tang, S., Ye, M., Xu, P. et al. Adaptive pedestrian detection by predicting classifier. Neural Comput & Applic 31, 1189–1200 (2019). https://doi.org/10.1007/s00521-017-3152-z

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