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Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery

Gaus, Y.F.A.; Bhowmik, N.; Issac-Medina, B.K.S.; Atapour-Abarghouei, A.; Shum, H.P.H; Breckon, T.P.

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery Thumbnail


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Abstract

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample size, and inadequate distribution coverage for the other class (abnormal). In this work, we propose the dual use of both visual appearance and localized motion characteristics, derived from optic flow, applied on a per-region basis to facilitate object-wise anomaly detection within this context. Leveraging established object localization techniques from a region proposal network, optic flow is extracted from each object region and combined with appearance in the far infrared (thermal) band to give a 3-channel spatiotemporal tensor representation for each object (1 × thermal - spatial appearance; 2 × optic flow magnitude as x and y components - temporal motion). This formulation is used as the basis for training contemporary semi-supervised anomaly detection approaches in a region-based manner such that anomalous objects can be detected as a combination of appearance and/or motion within the scene. Evaluation is performed using the LongTerm infrared (thermal) Imaging (LTD) benchmark dataset against which successful detection of both anomalous object appearance and motion characteristics are demonstrated using a range of semi-supervised anomaly detection approaches.

Citation

Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00301

Conference Name IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023
Conference Location Vancouver, BC
Start Date Jun 17, 2023
End Date Jun 24, 2023
Acceptance Date Apr 6, 2023
Online Publication Date Aug 14, 2023
Publication Date 2023
Deposit Date Apr 18, 2023
Publicly Available Date Aug 14, 2023
Publisher Institute of Electrical and Electronics Engineers
Series ISSN 2160-7508
Book Title 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISBN 9798350302509
DOI https://doi.org/10.1109/CVPRW59228.2023.00301
Public URL https://durham-repository.worktribe.com/output/1134121
Publisher URL https://ieeexplore.ieee.org/xpl/conhome/1000147/all-proceedings

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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.





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