The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach
Abstract
Society is rapidly accepting the use of video cameras in many new and varied locations, but effective methods to utilize and manage the massive resulting amounts of visual data are only slowly developing. This paper presents a framework for live video analysis in which the behaviors of surveillance subjects are described using a vocabulary learned from recurrent motion patterns, for real-time characterization and prediction of future activities, as well as the detection of abnormalities. The repetitive nature of object trajectories is utilized to automatically build activity models in a 3-stage hierarchical learning process. Interesting nodes are learned through Gaussian mixture modeling, connecting routes formed through trajectory clustering, and spatio-temporal dynamics of activities probabilistically encoded using hidden Markov models. Activity models are adapted to small temporal variations in an online fashion using maximum likelihood regression and new behaviors are discovered from a periodic retraining for long-term monitoring. Extensive evaluation on various data sets, typically missing from other work, demonstrates the efficacy and generality of the proposed framework for surveillance-based activity analysis.
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Cited by
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00240 · Cited by 2,466
Adversarially Learned One-Class Classifier for Novelty Detection
Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765
Pallotta, G., Vespe, M., & Bryan, K. (2013) · Entropy · doi:10.3390/e15062218 · Cited by 695
A Review of Human Activity Recognition Methods
Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00028 · Cited by 566
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., & Klette, R. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.02.006 · Cited by 470
Metadata
- Authors
- Morris, B. T., & Trivedi, M. M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2011-11
- Volume
- 33
- Issue
- 11
- Pages
- 2287-2301
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2092272787
- Cited by
- 349
- References
- 35
- FWCI
- 16.56
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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