Bio-Inspired Video Enhancement for Small Moving Target Detection
Abstract
Moving targets at a very large distance from a camera appear small and of low contrast. The low signal-to-noise-ratio and the presence of clutter in the background degrade the detection performance of conventional moving object detection techniques. To address these challenges, we propose temporal pre-processing of video frames using a biologically-inspired vision model. The bio-inspired model consists of multiple layers of processing analogous to the photoreceptor cells in the visual system of small insects. The adaptive filtering mechanism in the photoreceptor cells suppresses clutter and expands the possible range of input signal changes which improves the target background contrast. We perform experiments on real world video sequences of small moving targets captured with a high bit depth, high resolution and high frame-rate camera. Experimental results show that the biological vision based pre-processing leads to improved detection performance when used in conjunction with a variety of computer vision based moving object detection algorithms. The temporal bio-processing alone has improved the area under the receiver operating characteristic (AUROC) curve of the best performing algorithm by 75.4%. Our results suggest that the bio-inspired pre-processing has strong potential to become a key component of a practical small target detection system.
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Although AUC has been used to classify the accuracy of target detection algorithms, its extension to multiclass classification is currently not totally understood.
“Although AUC has been used to classify the accuracy of target detection algorithms [86], its extension to the analysis of multiclass classification is currently not totally understood [80,87].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [86]
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- Authors
- Uzair, M., Brinkworth, R. S., & Finn, A.
- Affiliations
- University of South Australia (AU)
- Flinders University (AU)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2021
- Volume
- 30
- Pages
- 1232-1244
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W3113124545
- Cited by
- 42
- References
- 56
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 6 · 2023: 12 · 2022: 9 · 2021: 5
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Funders
- Australian Defense Science and Technology (DST) Group and Next Generation Technology Fund (NGTF) under their Counter Improvised Threat (CIT) Grand Challenge (013)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Aerospace Engineering › Infrared Target Detection Methodologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- bio-inspired vision, insect photoreceptors, clutter suppression, adaptive filtering, low signal-to-noise ratio, moving object detection, video enhancement
- MeSH terms
- Algorithms, Image Processing, Computer-Assisted, Models, Biological, Motion, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, Video Recording
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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