Abstract
Scene understanding in smart surveillance and security is one of the major fields of investigation in computer vision research and industry. The ability of a system to automatically analyze and learn the events that occur within a scene (e.g., a running person, a parking car) is conditioned by several complex aspects such as feature extraction, tracking and recognition. One of the most important aspects in the event learning process is the detection of the time interval in which an event occurs (i.e., when it starts and ends). The present paper is focused on the learning of temporal correlated events. In particular, a formalized description of the features associated with each event and the linked strategy to define the event time-line are provided. The paper also reports preliminary tests carried out on videos related to a reference outdoor environment which validate the proposed strategy.
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References
Martinel, N., Micheloni, C., Piciarelli, C.: Pre-Emptive camera activation for Video Surveillance HCI. In: Intenational Conference on Image Analysis and Processing, Ravenna, RA, pp. 189–198, September 2011
Martinel, N., Micheloni, C., Piciarelli, C., Foresti, G.L.: Camera Selection for Adaptive Human-Computer Interface. IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(5), 653–664 (2014)
Martinel, N., Micheloni, C.: Sparse matching of random patches for person Re-identification. In: International Conference on Distributed Smart Cameras (2014)
Martinel, N., Micheloni, C.: Classification of Local Eigen-Dissimilarities for Person Re-Identification. IEEE Signal Processing Letters 22(4), 455–459 (2015)
Piciarelli, C., Micheloni, C., Martinel, N., Vernier, M., Foresti, G.L.: Outdoor environment monitoring with unmanned aerial vehicles. In: International Conference on Image Analysis and Processing (2013)
Fookes, C., Denman, S., Lakemond, R., Ryan, D., Sridharan, S., Piccardi, M.: Semi-supervised intelligent surveillance system for secure environments. In: IEEE International Symposium on Industrial Electronics, pp. 2815–2820 (2010)
Zhong, H.Z.H., Shi, J.S.J., Visontai, M.: Detecting unusual activity in video. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2 (2004)
Zhao, B., Fei-Fei, L., Xing, E.P.: Online detection of unusual events in videos via dynamic sparse coding. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3313–3320 (2011)
Xu, J., Denman, S., Fookes, C., Sridharan, S.: Unusual event detection in crowded scenes using bag of LBPs in spatio-temporal patches. In: Proceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011, pp. 549–554 (2011)
Ghanem, N., DeMenthon, D., Doermann, D., Davis, L.: Representation and recognition of events in surveillance video using petri nets. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop (2004)
Nevatia, R., Hobbs, J., Bolles, B.: An ontology for video event representation. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop (2004)
Hakeem, A., Shah, M.: Learning, detection and representation of multi-agent events in videos. Artificial Intelligence 171(8–9), 586–605 (2007)
Lin, L., Gong, H., Li, L., Wang, L.: Semantic event representation and recognition using syntactic attribute graph grammar. Pattern Recognition Letters 30(2), 180–186 (2009)
Vernier, M., Martinel, N., Micheloni, C., Foresti, G.L.: Remote feature learning for mobile Re-identification. In: International Conference on Distributed Smart Cameras, pp. 1–6. Palm Springs, CA, IEEE, October 2013
Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-Based Anomalous Event Detection. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1544–1554 (2008)
Micheloni, C., Snidaro, L., Foresti, G.L.: Exploiting Temporal Statistics for Events Analysis and Understanding. Image and Vision Computing 27(10), 1459–1469 (2009)
Agarwal, C., Sharma, A.: Image understanding using decision tree based machine learning. In: ICIMU 2011 : Proceedings of the 5th international Conference on Information Technology & Multimedia, pp. 1–8 (2011)
Fischer, Y., Beyerer, J.: Defining dynamic Bayesian networks for probabilistic situation assessment. In: International Conference on Information Fusion, pp. 888–895 (2012)
Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.C., Lee, J.T., Mukherjee, S., Aggarwal, J.K., Lee, H., Davis, L., Swears, E., Wang, X., Ji, Q., Reddy, K., Shah, M., Vondrick, C., Pirsiavash, H., Ramanan, D., Yuen, J., Torralba, A., Song, B., Fong, A., Roy-Chowdhury, A., Desai, M.: A large-scale benchmark dataset for event recognition in surveillance video. In: International Conference on Computer Vision and Pattern Recognition, pp. 3153–3160 (2011)
Veeraraghavan, H., Papanikolopoulos, N., Schrater, P.: Learning dynamic event descriptions in image sequences. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007)
Joo, S.W., Chellappa, R.: Attribute grammar-based event recognition and anomaly detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2006 (2006)
Moore, D., Essa, I.: Recognizing multitasked activities from video using stochastic context-free grammar. In: AAAI National Conf. on AI, pp. 770–776 (2002)
Martinel, N., Micheloni, C., Foresti, G.L.: Robust Painting Recognition and Registration for Mobile Augmented Reality. IEEE Signal Processing Letters 20(11), 1022–1025 (2013)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: International Conference on Computer Vision, Ieee, pp. 1–8 (2007)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: International Conference on Computer Vision and Pattern Recognition (CVPR) vol. 2, pp. 2169–2178 (2006)
Foresti, G.L., Dolso, T.: Adaptive High-Order Neural Trees for Pattern Recognition. IEEE Transactions on System, Man and Cybernetics Part B 34(2), 988–996 (2004)
Piciarelli, C., Micheloni, C., Foresti, G.L.: PTZ Camera Network Reconfiguration. In: Third ACM/IEEE International Conference on Distributed Smart Cameras, Como, Italy (2009)
Martinel, N., Micheloni, C., Piciarelli, C.: Distributed Signature Fusion for Person Re-identification. In: International Conference on Distributed Smart Cameras, Hong Kong, pp. 1–6 (2012)
Garcia, J., Martinel, N., Foresti, G.L., Gardel, A., Micheloni, C.: Person orientation and feature distances boost Re-identification. In: International Conference on Pattern Recognition (2014)
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Martinel, N. et al. (2015). Selection of Temporal Features for Event Detection in Smart Security. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9280. Springer, Cham. https://doi.org/10.1007/978-3-319-23234-8_56
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DOI: https://doi.org/10.1007/978-3-319-23234-8_56
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