The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Anomaly Detection and Localization in Crowded Scenes
Abstract
The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results.
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Cites
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“expectation IEðYjX; Þ1⁄2F@ ðy; xðnÞÞ by F@ ðŷ; xðnÞÞ, where ŷ is the “evil twin” of the ground-truth label field yðnÞ [41].”
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Cited by
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
Adversarially Learned One-Class Classifier for Novelty Detection
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Latent Space Autoregression for Novelty Detection
Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00057 · Cited by 508
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
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence
Nguyen, T. N., & Meunier, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00136 · Cited by 459
Metadata
- Authors
- Li, W., Mahadevan, V., & Vasconcelos, N.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2014-01
- Volume
- 36
- Issue
- 1
- Pages
- 18-32
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1967456674
- Cited by
- 1,017
- References
- 41
- FWCI
- 43.44
- Type
- journal article
- Language
- en
- Open access
- closed
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