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Realtime and robust object matching with a large number of templates

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Abstract

Most of conventional object matching methods are based on comparing local features, which are too computational demanding. Recently, Dominant Orientation Templates (DOT) were proposed to solve the efficiency issue. Although DOT obtains promising results, it still suffers the problem of wasting too many bits in representation and fragility when partial occlusion occurs. As the number of templates increase, the performance will decrease. Therefore, we propose a compact DOT representation with a fast partial occlusion handling approach. Instead of using seven orientations in the original implementation, we employ single orientation of the highest gradients for the proposed compact DOT representation (C-DOT). Consequently, the size of feature vectors is reduced from 8 bits to 3 bits. To efficiently tackle the partial occlusion, we introduce the C-DOT similarity map to store the matching scores of individual grids in each sliding window, which is used to further infer the occlusion map. The experimental results demonstrate that the proposed method outperforms DOT.

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Acknowledgments

This work is supported by the Natural Science Foundation of China (61472110, 61202145, 61100104 and 61065007), the Program for New Century Excellent Talents in University (No. NECT-12-0323) and the Natural Science Foundation of Fujian Province of China (2012J01287 and 2014J01256).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

    Chaoqun Hong & Xuhui Chen

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Jianke Zhu

  3. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Jun Yu

  4. Shenzhen Institues of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Jun Cheng

  5. The Chinese University of HongKong, Shatin, HongKong, China

    Jun Cheng

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  1. Chaoqun Hong
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  2. Jianke Zhu
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  3. Jun Yu
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  4. Jun Cheng
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  5. Xuhui Chen
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Correspondence to Jun Yu.

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Hong, C., Zhu, J., Yu, J. et al. Realtime and robust object matching with a large number of templates. Multimed Tools Appl 75, 1459–1480 (2016). https://doi.org/10.1007/s11042-014-2305-7

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