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Real-time multi-class object detection using two-dimensional index

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Abstract

When there exists only one sample for each category of objects, previous approaches of training multi-class classifiers are not applicable. In this paper, we propose a new template matching method that is both robust and real-time to multi-class object detections. Firstly, object features are encoded as binary codes based on both quantized gradient intensity and quantized gradient orientation mappings. Then, a two-dimensional index table is constructed. This two-dimensional index table has advantages in effectively organizing relationships between the features from the multi-class templates and their corresponding locations in the templates. For a target image, the features are firstly encoded. Then the object is localized by voting based on the queries of features from the index table. Our experiments on two public data sets demonstrate the high efficiency of our method and the superior performance to the state-of-the-art methods.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61375038).

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Authors and Affiliations

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

    Yumin Dou, Pei Xu, Mao Ye, Xue Li, Lishen Pei & Xudong Li

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  1. Yumin Dou
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  2. Pei Xu
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  3. Mao Ye
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  4. Xue Li
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  5. Lishen Pei
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  6. Xudong Li
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Correspondence to Mao Ye.

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Dou, Y., Xu, P., Ye, M. et al. Real-time multi-class object detection using two-dimensional index. J Real-Time Image Proc 16, 243–253 (2019). https://doi.org/10.1007/s11554-015-0525-3

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  • DOI: https://doi.org/10.1007/s11554-015-0525-3

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