The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning patterns of activity using real-time tracking
Abstract
Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activity classification, and event detection. In this paper, we focus on motion tracking and show how one can use observed motion to learn patterns of activity in a site. Motion segmentation is based on an adaptive background subtraction method that models each pixel as a mixture of Gaussians and uses an online approximation to update the model. The Gaussian distributions are then evaluated to determine which are most likely to result from a background process. This yields a stable, real-time outdoor tracker that reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by accumulating joint co-occurrences of the representations within a sequence. These joint co-occurrence statistics are then used to create a hierarchical binary-tree classification of the representations. This method is useful for classifying sequences, as well as individual instances of activities in a site.
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Citations
Cites
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Cited by
A survey of appearance models in visual object tracking
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508039 · Cited by 705
Trajectory-Based Anomalous Event Detection
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Discovering Activities to Recognize and Track in a Smart Environment
Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.148 · Cited by 462
Learning Motion Patterns of People for Compliant Robot Motion
Bennewitz, M., Burgard, W., Cielniak, G., & Thrun, S. (2005) · The International Journal of Robotics Research · doi:10.1177/0278364904048962 · Cited by 399
Ontology‐based activity recognition in intelligent pervasive environments
Chen, L., & Nugent, C. (2009) · International Journal of Web Information Systems · doi:10.1108/17440080911006199 · Cited by 253
Understanding human intentions via hidden markov models in autonomous mobile robots
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Hierarchical and Networked Vehicle Surveillance in ITS: A Survey
Tian, B., Morris, B. T., Tang, M., Liu, Y., Yao, Y., Gou, C., Shen, D., & Tang, S. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2552778 · Cited by 160
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Cardinaux, F., Bhowmik, D., Abhayaratne, C., & Hawley, M. S. (2011) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-2011-0110 · Cited by 98
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Object Tracking in Structured Environments for Video Surveillance Applications
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Information-theoretic active scene exploration
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Recognition of human activity through hierarchical stochastic learning
Luhr, S., Bui, H. H., Venkatesh, S., & West, G. A. W. (2004) · Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003). · doi:10.1109/percom.2003.1192766 · Cited by 58
NIC: A Robust Background Extraction Algorithm for Foreground Detection in Dynamic Scenes
Huynh-The, T., Banos, O., Lee, S., Kang, B. H., Kim, E.-S., & Le-Tien, T. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2543118 · Cited by 51
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Metadata
- Authors
- Stauffer, C., & Grimson, W. E. L.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2000
- Volume
- 22
- Issue
- 8
- Pages
- 747-757
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2111918405
- Cited by
- 3,248
- References
- 1
- FWCI
- 39.45
- Type
- journal article
- Language
- en
- Open access
- closed
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