Fusing Multimodal Video Data for Detecting Moving Objects/Targets in Challenging Indoor and Outdoor Scenes
Open accessAbstract
Single sensor systems and standard optical—usually RGB CCTV video cameras—fail to provide adequate observations, or the amount of spectral information required to build rich, expressive, discriminative features for object detection and tracking tasks in challenging outdoor and indoor scenes under various environmental/illumination conditions. Towards this direction, we have designed a multisensor system based on thermal, shortwave infrared, and hyperspectral video sensors and propose a processing pipeline able to perform in real-time object detection tasks despite the huge amount of the concurrently acquired video streams. In particular, in order to avoid the computationally intensive coregistration of the hyperspectral data with other imaging modalities, the initially detected targets are projected through a local coordinate system on the hypercube image plane. Regarding the object detection, a detector-agnostic procedure has been developed, integrating both unsupervised (background subtraction) and supervised (deep learning convolutional neural networks) techniques for validation purposes. The detected and verified targets are extracted through the fusion and data association steps based on temporal spectral signatures of both target and background. The quite promising experimental results in challenging indoor and outdoor scenes indicated the robust and efficient performance of the developed methodology under different conditions like fog, smoke, and illumination changes.
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Several multimodal variants of multi-object tracking have been proposed to improve the speed and accuracy of visual tracking by using multiple data sources.
“To date, several multimodal variants of MOT have been proposed to improve the speed and accuracy of visual tracking by using multiple data sources, e.g., thermal, shortwave infrared, and hyperspectral data [195], RGB and thermal data [196], RGB and infrared data [197], etc.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [195]
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Research works printed but not linked: 1
- He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 27-29 October 2017.
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Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
- Authors
- Kandylakis, Z., Vasili, K., & Karantzalos, K.
- Affiliations
- National Technical University of Athens (GR)
- Corresponding author
- Kandylakis, Zacharias
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2019-02-21
- Volume
- 11
- Issue
- 4
- Pages
- 446
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2915599493
- Cited by
- 15
- References
- 27
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 2 · 2020: 1 · 2019: 1
- FWCI
- 0.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (313161, 607292)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Engineering › Aerospace Engineering › Infrared Target Detection Methodologies
- Keywords
- moving object detection, infrared thermography, shortwave infrared, hyperspectral imaging, multi-sensor systems, real-time object detection, background subtraction, convolutional neural networks, data association, spectral signatures, indoor and outdoor scenes, illumination variation
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