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Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes
Joint Authors
Hu, Xing
Hu, Shiqiang
Zhang, Xiaoyu
Zhang, Huanlong
Luo, Lingkun
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-03
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes.
Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages.
First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event.
Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor.
Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided.
We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets.
Experimental results show the promising performance of LNND-based method against the state-of-the-art methods.
It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.
American Psychological Association (APA)
Hu, Xing& Hu, Shiqiang& Zhang, Xiaoyu& Zhang, Huanlong& Luo, Lingkun. 2014. Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1050432
Modern Language Association (MLA)
Hu, Xing…[et al.]. Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1050432
American Medical Association (AMA)
Hu, Xing& Hu, Shiqiang& Zhang, Xiaoyu& Zhang, Huanlong& Luo, Lingkun. Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1050432
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050432