Machine Learning Based Human Activity Recognition in Video Surveillance

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Published Nov 15, 2021
https://doi.org/10.47164/ijngc.v12i4.382
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Volume 12, Issue 4, November 2021

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Megha Gupta
Amarjit Malhotra
Sarthak Sahlot
Rishabh Attri
Rohan Kumar

Abstract




In the current time there is a massive shift of technology from analog to digital. According to previous facts, three quarters of world data was in analog form. But now, approximately it is preserved in digital form. Due to this rapid growth of technology there is a massive growth of image data being generated by surveillance cameras. Automated anomaly detection has become necessary in order to detect the presence of any kind of dangerous activities such as robbery, road accidents and many more. Recently, machine Learning approaches have achieved the state of the art results in many tasks related to the automated anomaly detection process. The objective of this paper is to propose one such efficient method for anomaly detection. The proposed approach works with multiple instance learning with I3D feature extractor and crop augmented images. The obtained AUC prove the proposed approach to be superior of previous models.




 

 

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Gupta, M., Amarjit Malhotra, Sarthak Sahlot, Rishabh Attri, & Rohan Kumar. (2021). Machine Learning Based Human Activity Recognition in Video Surveillance. International Journal of Next-Generation Computing, 12(4). https://doi.org/10.47164/ijngc.v12i4.382
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References

  1. Mehrsan, J., Martin , D. 2013. An on-line, real-time learning method for detecting anomalies
  2. in videos using spatio-temporal compositions Computer Vision and Image Understand- ing Vol.17, pp.1436–1452.
  3. M. Ribeiro, M. Gutoski, A. E. Lazzaretti and H. S. Lopes 2020. One-Class Classification in Images and Videos Using a Convolutional Autoencoder With Compact Embedding. IEEE Access Vol.8 , pp.. 86520- -86535. DOI: https://doi.org/10.1109/ACCESS.2020.2992804
  4. L. Wang, F. Zhou, Z. Li, W. Zuo and H. Tan 2018. Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder. In 25th IEEE International Conference on Image Processing (ICIP), Athens. pp.2276- -2280. DOI: https://doi.org/10.1109/ICIP.2018.8451070
  5. W. Sultani, C. Chen and M. Shah 2018. Real-World Anomaly Detection in Surveillance Videos. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT. pp.6479- -6488. DOI: https://doi.org/10.1109/CVPR.2018.00678
  6. Vu, H., Nguyen, T. D., Travers, A., Venkatesh, S., and Phung, D 2017. Energy-based localized anomaly detection in video surveillance In J. Kim, K. Shim, L. Cao, J-G. Lee, DOI: https://doi.org/10.1007/978-3-319-57454-7_50
  7. X. Lin, and Y-S. Moon (Eds.), Advances in Knowledge Discovery and Data Mining.
  8. S. Cosar, G. Donatiello, V. Bogorny, C. Garate, L. O. Alvares and F. Bremond 2017. Toward Abnormal Trajectory and Event Detection in Video Surveillance. IEEE Transactions on Circuits and Systems for Video Technology Vol.27 , pp.683- -695. DOI: https://doi.org/10.1109/TCSVT.2016.2589859
  9. J. T. Zhou, J. Du, H. Zhu, X. Peng, Y. Liu and R. S. M. Goh 2019. AnomalyNet: An Anomaly Detection Network for Video Surveillance. IEEE Transactions on Information Forensics and Security Vol.14 , pp.2537- -2550. DOI: https://doi.org/10.1109/TIFS.2019.2900907
  10. C. E. Au, S. Skaff and J. J. Clark 2006. Anomaly Detection for Video Surveillance Appli- cations. In 18th International Conference on Pattern Recognition (ICPR’06), Hong Kong. pp.888 - - 891.
  11. Li Nannan, Wu Xinyu, Guo Huiwen, Xu Dan, Ou Yongsheng and Chen Yen-Lun 2015.
  12. Anomaly Detection in Video Surveillance via Gaussian Process. International Journal of Pattern Recognition and Artificial Intelligence
  13. M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis 2016. Learning temporal regularity in video sequences. In CVPR, June 2016 DOI: https://doi.org/10.1109/CVPR.2016.86
  14. C. Lu, J. Shi, and J. Jia 2013. Abnormal event detection at 150 fps in matlab. In ICCV, 2013 DOI: https://doi.org/10.1109/ICCV.2013.338
  15. UCF-Anomaly-Detection-Dataset .
  16. J. Carreira and A. Zisserman 2017. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI , pp. 4724 - - 4733. DOI: https://doi.org/10.1109/CVPR.2017.502