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, Volume 77, Issue 22, pp 29919–29932 | Cite as

Object detection and tracking benchmark in industry based on improved correlation filter

  • Shangzhen Luan
  • Yan Li
  • Xiaodi Wang
  • Baochang Zhang
Article
  • 140 Downloads

Abstract

Real-time object detection and tracking have shown to be the basis of intelligent production for industrial 4.0 applications. It is a challenging task because of various distorted data in complex industrial setting. The correlation filter (CF) has been used to trade off the low-cost computation and high performance. However, traditional CF training strategy can not get satisfied performance for the various industrial data; because the simple sampling(bagging) during training process will not find the exact solutions in a data space with a large diversity. In this paper, we propose Dijkstra-distance based correlation filters (DBCF), which establishes a new learning framework that embeds distribution-related constraints into the multi-channel correlation filters (MCCF). DBCF is able to handle the huge variations existing in the industrial data by improving those constraints based on the shortest path among all solutions. To evaluate DBCF, we build a new dataset as the benchmark for industrial 4.0 application. Extensive experiments demonstrate that DBCF produces high performance and exceeds the state-of-the-art methods. The dataset and source code can be found at https://github.com/bczhangbczhang.

Keywords

Object detection Tracking Correlation filter Industry 4.0 Tracking in industry 

Notes

Acknowledgements

The work was supported by the Natural Science Foundation of China under Contract 61672079 and 61473086, and Shenzhen Peacock Plan KQTD2016112515134654. This work is supported by the Open Projects Program of National Laboratory of Pattern Recognition, and shenzhen peacock plan. Shangzhen Luan and Yan Li have the same contribution to this paper. We also want to thank Professor Zhu Lei of Hangzhou Dianzi University, who participated in writing and technical editing of the manuscript.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Shangzhen Luan
    • 1
  • Yan Li
    • 2
  • Xiaodi Wang
    • 1
  • Baochang Zhang
    • 1
    • 3
  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.School of Electronics and Information EngineeringBeihang UniversityBeijingChina
  3. 3.Shenzhen Academy of Aerospace TechnologyShenzhenChina

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