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Detection of dynamic background due to swaying movements from motion features

Pham, Duc-Son, Arandjelović, Ognjen and Venkatesh, Svetha 2015, Detection of dynamic background due to swaying movements from motion features, IEEE transactions on image processing, vol. 24, no. 1, pp. 332-344, doi: 10.1109/TIP.2014.2378034.

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Title Detection of dynamic background due to swaying movements from motion features
Author(s) Pham, Duc-Son
Arandjelović, Ognjen
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name IEEE transactions on image processing
Volume number 24
Issue number 1
Start page 332
End page 344
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-01
ISSN 1941-0042
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
Dynamic background
detection algorithms
shadow
motion-based analysis
convex optimization
ADMM
sparsity learning
ROC
mixture of Gaussians
OBJECT DETECTION
COMPRESSED DATA
REAL-TIME
SURVEILLANCE
EFFICIENT
RECOVERY
SCENES
TREES
VIDEO
Summary Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in the dynamic background literature.
Language eng
DOI 10.1109/TIP.2014.2378034
Field of Research 080109 Pattern Recognition and Data Mining
0801 Artificial Intelligence And Image Processing
0906 Electrical And Electronic Engineering
1702 Cognitive Science
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077469

Document type: Journal Article
Collection: Centre for Pattern Recognition and Data Analytics
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Citation counts: TR Web of Science Citation Count  Cited 10 times in TR Web of Science
Scopus Citation Count Cited 13 times in Scopus
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