The Journal of Korean Institute of Next Generation Computing (한국차세대컴퓨팅학회논문지)

Korean Institute of Next Generation Computing (한국차세대컴퓨팅학회)
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Crowd Behavior Detection using Convolutional Neural Network

컨볼루션 뉴럴 네트워크를 이용한 군중 행동 감지

  • Received : 2019.07.08
  • Accepted : 2019.11.15
  • Published : 2019.12.31

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Abstract

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

감시 영상에서 군중 행동의 자동 모니터링 및 감지는 보안, 안전 및 자산 보호와 같은 방대한 응용 프로그램으로 인해 컴퓨터 비전 분야에서 중요한 관심을 받고 있다. 또한 연구 커뮤니티에서 군중 분석 분야가 점차 증가하고 있다. 이를 위해서는 군중들의 행동을 감지하고 분석하는 것이 매우 필요하다. 본 논문에서는 스마트 시티에 설치된 감시 카메라의 비정상적인 활동을 감지하는 딥러닝 기반 방법을 제안하였다. 미세 조정된 VGG-16모델은 트레이닝된 공개적으로 사용 가능한 벤치마크 군중 데이터 셋을 실시간 스트리밍으로 테스트한다. CCTV카메라는 비디오 스트림을 캡쳐하는데, 비정상적인 활동이 감지되면 경보가 발생하여 추가 손실 전에 즉각적인 조치가 이루어지도록 가장 가까운 경찰서로 전송된다. 우리는 제안된 방법이 기존의 첨단 기술 보다 성능이 뛰어남을 실험으로 입증하였다.

Keywords

References

  1. Sultani, W., C. Chen, and M. Shah. Real-world anomaly detection in surveillance videos. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
  2. Bilinski, P. and F. Bremond. Human violence recognition and detection in surveillance videos. in 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). 2016. IEEE.
  3. Nievas, E.B., et al. Violence detection in video using computer vision techniques. in International conference on Computer analysis of images and patterns. 2011. Springer.
  4. Nam, J., M. Alghoniemy, and A.H. Tewfik. Audio-visual content-based violent scene characterization. in Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No. 98CB36269). 1998. IEEE.
  5. Lin, J., Y. Sun, and W. Wang. Violence detection in movies with auditory and visual cues. in 2010 International Conference on Computational Intelligence and Security. 2010. IEEE.
  6. Zajdel, W., et al. CASSANDRA: audio-video sensor fusion for aggression detection. in 2007 IEEE conference on advanced video and signal based surveillance. 2007. IEEE.
  7. Sajjad, M., et al., Raspberry Pi assisted facial expression recognition framework for smart security in law-enforcement services. Information Sciences, 2019. 479: p. 416-431. https://doi.org/10.1016/j.ins.2018.07.027
  8. Ullah, A., et al., Action recognition in video sequences using deep bi-directional LSTM with CNN features. IEEE Access, 2017. 6: p. 1155-1166. https://doi.org/10.1109/ACCESS.2017.2778011
  9. Sun, L., et al. Human action recognition using in Proceedings of the IEEE international conference on computer vision. 2015.
  10. Ullah, F.U.M., et al., Violence detection using spatiotemporal features with 3D convolutional neural network. Sensors, 2019. 19(11): p. 2472. https://doi.org/10.3390/s19112472
  11. Benabbas, Y., N. Ihaddadene, and C. Djeraba, Motion pattern extraction and event detection for automatic visual surveillance. EURASIP Journal on Image and Video Processing, 2011. 2011(1): p. 163682.
  12. Hassner, T., Y. Itcher, and O. Kliper-Gross. Violent flows: Real-time detection of violent crowd behavior. in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2012. IEEE.
  13. Adam, A., et al., Robust real-time unusual event detection using multiple fixed-location monitors. IEEE transactions on pattern analysis and machine intelligence, 2008. 30 (3): p. 555-560. https://doi.org/10.1109/TPAMI.2007.70825
  14. Blunsden, S. and R. Fisher, The BEHAVE video dataset: ground truthed video for multi-person behavior classification. Annals of the BMVA, 2010. 4(1-12): p. 4.
  15. Grega, M., et al., Automated detection of firearms and knives in a CCTV image. Sensors, 2016. 16(1): p. 47. https://doi.org/10.3390/s16010047
  16. Zhou, P., et al. Violent interaction detection in video based on deep learning. in Journal of Physics: Conference Series. 2017. IOP Publishing.
  17. Veenendaal, A., et al., Fight and Aggression Recognition using Depth and Motion Data. Computer Science and Emerging Research Journal, 2016. 4.
  18. Ravanbakhsh, M., et al. Plug-and-play cnn for crowd motion analysis: An application in abnormal event detection. in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). 2018. IEEE.
  19. Amin Ullah, Jamil Ahmad, Khan Muhammad, Irfan Mehmood, Mi Young Lee, Jun Ryeol Park, Sung Wook Baik. (2017). Action Recognition in Movie Scenes using Deep Features of Keyframes. THE JOURNAL OF KOREAN INSTITUTE OF NEXT GENERATION COMPUTING, 13(3), 7-14
  20. Fath U Min Ullah, Amin Ullah, Khan Muhammad, Mi Young Lee, Sung Wook Baik. (2018). Violence Recognition using Deep CNN for Smart Surveillance Applications. THE JOURNAL OF KOREAN INSTITUTE OF NEXT GENERATION COMPUTING, 14(5), 53-59.
  21. Amin Ullah, Nasir Rahim, Jamil Ahmad, Mi Young Lee, Sung Wook Baik. (2017). Analyzing Pedestrian Parts using Deep Features for Person Re-Identification. THE JOURNAL OF KOREAN INSTITUTE OF NEXT GENERATION COMPUTING, 13(2), 82-92.