Abstract
This paper proposed a key clip localization algorithm to classify the potential clip that might contain different anomalous activities from whole surveillance video. The presented technique extracts the physical motion of object by computing dense optic flow. All statistical properties of motion component in different directions were jointly encoded to represent the feature vector of frames. The feature vectors were sent to soft-margin SVM classifier for training. Finally, a double-threshold technique is applied to merge separate proposals of frame and clip. This model is evaluated on a large-scale dataset namely UCF-crime. The dataset covers 13 kind of anomalous events in real-world long surveillance videos. Ultimately, the classifier is an efficient system with high sensitivity. The high-quality video clip proposals are helpful for further anomaly detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, H., Han, Z.: Catching attacker(s) for collaborative spectrum sensing in cognitive radio systems: an abnormality detection approach. In: Proceedings of IEEE Symposium on New Frontiers in Dynamic Spectrum, Singapore, p. 12 (2010)
Fragkiadakis, A., Tragos, E., Askoxylakis, I.: A survey on security threats and detection techniques in cognitive radio networks. IEEE Commun. Surv. Tutorials 15(1), 428–445 (2013)
Wang, W., Chen, L., Shin, K., Duan, L.: Thwarting intelligent malicious behaviors in cooperative spectrum sensing. IEEE Transactions on Mobile
Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: CVPR 2011, pp. 3449–3456, June 2011
Zhao, B., Fei-Fei, L., Xing, E.P.: Online detection of unusual events in videos via dynamic sparse coding. CVPR 2011, 3313–3320 ( 2011)
Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 FPS in matlab. In: 2013 IEEE International Conference on Computer Vision, pp. 2720–2727, December 2013
Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked RNN framework. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 341–349, October 2017
Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1975–1981, June 2010
Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921– 2928, June 2009
Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2008
Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1544–1554 (2008)
Tianzhu Zhang, Hanqing Lu, and Stan Z. Li. Learning semantic scene models by object classification and trajectory clustering. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1940–1947, June 2009
Nguyen, T.-N., Meunier, J.: Anomaly detection in video sequence with appearance-motion correspondence. In: Proceedings of the IEEE International Conference on Computer Vision (2019)
Li, M., et al.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
Pang, G., Shen, C., van den Hengel, A.: Deep anomaly detection with deviation networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Hinami, R., Mei, T., Satoh, S.: Joint detection and recounting of abnormal events by learning deep generic knowledge. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3639–3647, October 2017
Xu, D., Yan, Y., Ricci, E., Sebe, N.: Detecting anomalous events in videos by learning deep representations of appearance and motion. Computer Vision and Image Understanding, 156, 117–112
Horn, B.K.P., Schunck. B.G.: Determining optical flow. Techniques and Applications of Image Understanding, vol. 281. International Society for Optics and Photonics (1981)
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. Scandinavian Conference on Image Analysis, Springer, Berlin, Heidelberg (2003)
Wan, V., Campbell, W.M.: Support vector machines for speaker verification and identification. In: Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No. 00TH8501), vol. 2. IEEE (2000)
Acknowledgments
• Research supported by Beijing Advanced Innovation Center for Intelligent Robots and Systems. Project Number is NO. 2018IRS20.
• Research Center of Security Video and Image Processing Engineering Technology of Guizhou, Project Number is No. SRC-Open Project ([2020]001])
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Y., Mei, X., Wu, X. (2021). Dense Optic Flow-Based Key Clip Localization in Surveillance Videos. In: Xu, Z., Parizi, R.M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing, vol 1342. Springer, Cham. https://doi.org/10.1007/978-3-030-70042-3_98
Download citation
DOI: https://doi.org/10.1007/978-3-030-70042-3_98
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70041-6
Online ISBN: 978-3-030-70042-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)