Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
권호 표지

A Research of CNN-based Object Detection for Multiple Object Tracking in Image

영상에서 다중 객체 추적을 위한 CNN 기반의 다중 객체 검출에 관한 연구

  • Ahn, Hyochang (Department of Energy IT Engineering, Far East University) ;
  • Lee, Yong-Hwan (Department of Digital Contents, Wonkwang University)
  • 안효창 (극동대학교 에너지IT학과) ;
  • 이용환 (원광대학교 디지털콘텐츠공학과)
  • Received : 2019.09.17
  • Accepted : 2019.09.26
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, video monitoring system technology has been rapidly developed to monitor and respond quickly to various situations. In particular, computer vision and related research are being actively carried out to track objects in the video. This paper proposes an efficient multiple objects detection method based on convolutional neural network (CNN) for multiple objects tracking. The results of the experiment show that multiple objects can be detected and tracked in the video in the proposed method, and that our method is also good performance in complex environments.

Keywords

References

  1. Yang, Hanxuan, et al., "Recent advances and trends in visual tracking: A review." Neurocomputing, 74(18), pp.3823-3831. 2011. https://doi.org/10.1016/j.neucom.2011.07.024
  2. Yilmaz, Alper, Omar Javed, and Mubarak Shah, "Object tracking: A survey." Acm computing surveys (CSUR), 38(4), 13. 2006. https://doi.org/10.1145/1177352.1177355
  3. Kuzanski, Marcin, et al., "Machine vision-automation of selected measurement systems." 2008 International Conference on Perspective Technologies and Methods in MEMS Design. IEEE, pp. 65-68, 2008.
  4. Joshi, Kinjal A., and Darshak G. Thakore, "A survey on moving object detection and tracking in video surveillance system." International Journal of Soft Computing and Engineering, 2(3), pp.44-48. 2012.
  5. Mishra, Pawan Kumar, and G. P. Saroha, "A study on video surveillance system for object detection and tracking." 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). pp. 221-226, 2016.
  6. Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas, "Tracking-learning-detection." IEEE transactions on pattern analysis and machine intelligence, 34(7), pp.1409-1422, 2012. https://doi.org/10.1109/TPAMI.2011.239
  7. Ahn, Hyochang, and Lee, Yong-Hwan, "Performance analysis of object recognition and tracking for the use of surveillance system." Journal of Ambient Intelligence and Humanized Computing, 7(5), pp.673-679, 2016. https://doi.org/10.1007/s12652-015-0325-4
  8. Collins, Robert T., et al., "A System for Video Surveillance and Monitoring", The Robotics Institute, Carnegie Mellon University, Pittsburgh PA, pp.1-68, 2000.
  9. Wu, Yi, Jongwoo Lim, and Ming-Hsuan Yang, "Object tracking benchmark." IEEE Transactions on Pattern Analysis and Machine Intelligence 37.9, pp.1834-1848, 2015. https://doi.org/10.1109/TPAMI.2014.2388226
  10. Allen, John G., Richard YD Xu, and Jesse S. Jin, "Object tracking using camshift algorithm and multiple quantized feature spaces." Proceedings of the Pan-Sydney area workshop on Visual information processing. Australian Computer Society, Inc., pp.3-7, 2004.
  11. Comaniciu, Dorin, Visvanathan Ramesh, and Peter Meer, "Kernel-based object tracking." IEEE Transactions on Pattern Analysis & Machine Intelligence, 25 (5), pp. 564-575, 2003. https://doi.org/10.1109/TPAMI.2003.1195991
  12. Cortes, Corinna, and Vladimir Vapnik, "Support-vector networks." Machine learning, 20.(3), pp. 273-297,1995. https://doi.org/10.1007/BF00994018
  13. Chellapilla, Kumar, Sidd Puri, and Patrice Simard, "High performance convolutional neural networks for document processing." 2006.
  14. Lee, YuKyong and Lee, Yong-Hwan, "Implementation of Moving Object Recognition based on Deep Learning." Journal of the Semiconductor & Display Technology 17.2, pp.67-70. 2018.
  15. Chu, Yeon Ho and Choi, Young Kyu, "A Deep Learning based IOT Device Recognition System", Journal of the Semiconductor & Display Technology 18.2 pp.1-15, 2019.
  16. Kim, Dong Hwan, et al., "Modeling with Thin Film Thickness using Machine Learning." Journal of the Semiconductor & Display Technology 18.2 pp.48-52, 2019.
  17. Redmon, Joseph, et al., "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779-788, 2016.
  18. Chen, Yan, et al., "CNNTracker: Online discriminative object tracking via deep convolutional neural network." Applied Soft Computing 38, pp.1088-1098. 2016. https://doi.org/10.1016/j.asoc.2015.06.048