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Video Object Detection with MeanShift Tracking

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Rough Sets (IJCRS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13633))

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Abstract

Video object detection, a basic task in the computer vision, is rapidly evolving and widely used in various real-world applications. Recently, with the success of deep learning, deep video object detection has become an important research direction. Although existing deep video object detection methods have achieved excellent results compared with those of traditional methods, they ignore the motion laws of objects and are hard to improve the detection performance of the fast moving objects suffering from deteriorated problems such as the motion blur, video defocus, object occlusion and rare poses. To address this limitation, we add the object trajectory information into the process of the video object detection and devise a novel deep video object detection method which utilizes the MeanShift algorithm to guide the deep neural networks to enhance the video object detection performance. The experiments on ImageNet VID dataset validate that the proposed method can improve the recognition performance of fast moving objects with taking into account the motion laws of objects.

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Acknowledgment

This work was supported by National Natural Science Foundation of China (Serial Nos. 61976134, 61991410, 61991415), Natural Science Foundation of Shanghai (Serial No. 21ZR1423900) and Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province, China (No. CICIP2021001).

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Shuai Zhang, Haijie Fu & Xiaodong Yue

  2. College of Electronics and Information Engineering, Tongji University, Shanghai, 200444, China

    Wei Liu

  3. Artificial Intelligence Institute of Shanghai University, Shanghai, China

    Shuai Zhang & Xiaodong Yue

Authors
  1. Shuai Zhang
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  2. Wei Liu
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  3. Haijie Fu
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  4. Xiaodong Yue
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Corresponding author

Correspondence to Xiaodong Yue .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    JingTao Yao

  2. Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  3. Shanghai University, Shanghai, China

    Xiaodong Yue

  4. Tongji University, Shanghai, China

    Duoqian Miao

  5. University of Kansas, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  6. Soochow University, Suzhou, Jiangsu, China

    Fanzhang Li

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Zhang, S., Liu, W., Fu, H., Yue, X. (2022). Video Object Detection with MeanShift Tracking. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_17

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  • DOI: https://doi.org/10.1007/978-3-031-21244-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21243-7

  • Online ISBN: 978-3-031-21244-4

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