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Multiple human tracking in drone image

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Abstract

Object tracking, especially human tracking is one of the challenging research problems in computer vision. Although the performance has gained some positive changes recently, there is still room for improvement. In this paper, we introduce an approach for human detection and tracking using Convolution Neural Network (CNN) and Hungarian Algorithm (HA). A CNN is used to localize multiple human beings from frame to frame in a video stream. This deep CNN is known as Faster R-CNN which achieved the state of the art performance in object detection problem. In the tracking process, we solve the data association problem in visual tracking using HA. A detected person will be assigned to a tracklet based on the data distribution in the video frame. The experimental results show that our system can deal with the videos captured from different scenarios in near real-time.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2018R1A1A1A05022526), (NRF-2017R1A4A1015559) and (NRF-2015R1D1A3A01019642).

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Authors and Affiliations

  1. School of Elec. and Comp. Eng., Chonnam National University, Gwangju, South Korea

    Hai Duong Nguyen, Soo Hyung Kim, Guee Sang Lee & Hyung Jeong Yang

  2. Software Convergence Education Institute, Chosun University, Gwangju, South Korea

    In Seop Na

  3. Division of Undeclared Majors, Chosun University, Gwangju, South Korea

    Jun Ho Choi

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  1. Hai Duong Nguyen
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  2. In Seop Na
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  3. Soo Hyung Kim
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  4. Guee Sang Lee
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  5. Hyung Jeong Yang
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  6. Jun Ho Choi
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Correspondence to In Seop Na.

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Nguyen, H.D., Na, I.S., Kim, S.H. et al. Multiple human tracking in drone image. Multimed Tools Appl 78, 4563–4577 (2019). https://doi.org/10.1007/s11042-018-6141-z

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  • DOI: https://doi.org/10.1007/s11042-018-6141-z

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