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Channel Coded Distribution Field Tracking for Thermal Infrared Imagery
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.ORCID iD: 0000-0002-6591-9400
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.ORCID iD: 0000-0002-6763-5487
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
2016 (English)In: PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), IEEE , 2016, p. 1248-1256Conference paper, Published paper (Refereed)
Abstract [en]

We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. The fast progress has been possible thanks to the development of new template-based tracking methods with online template updates, methods which have not been explored for TIR tracking. Instead, tracking methods used for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed. Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a template-based tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. In order to avoid background contamination of the object template, we propose to exploit background information for the online template update and to adaptively select the object region used for tracking. Moreover, we propose a novel method for estimating object scale change. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Further, the proposed tracker, ABCD, and the VOT-TIR2015 winner SRDCFir are evaluated on maritime data. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

Place, publisher, year, edition, pages
IEEE , 2016. p. 1248-1256
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-134402DOI: 10.1109/CVPRW.2016.158ISI: 000391572100151ISBN: 978-1-5090-1438-5 (print)ISBN: 978-1-5090-1437-8 (electronic)OAI: oai:DiVA.org:liu-134402DiVA, id: diva2:1072885
Conference
Computer Vision and Pattern Recognition Workshops (CVPRW), 2016 IEEE Conference on
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784EU, FP7, Seventh Framework Programme, 607567Available from: 2017-02-09 Created: 2017-02-09 Last updated: 2019-10-23
In thesis
1. Detection and Tracking in Thermal Infrared Imagery
Open this publication in new window or tab >>Detection and Tracking in Thermal Infrared Imagery
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as it is possible to measure a temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy.

This thesis addresses the problem of detection and tracking in thermal infrared imagery. Visual detection and tracking of objects in video are research areas that have been and currently are subject to extensive research. Indications oftheir popularity are recent benchmarks such as the annual Visual Object Tracking (VOT) challenges, the Object Tracking Benchmarks, the series of workshops on Performance Evaluation of Tracking and Surveillance (PETS), and the workshops on Change Detection. Benchmark results indicate that detection and tracking are still challenging problems.

A common belief is that detection and tracking in thermal infrared imagery is identical to detection and tracking in grayscale visual imagery. This thesis argues that the preceding allegation is not true. The characteristics of thermal infrared radiation and imagery pose certain challenges to image analysis algorithms. The thesis describes these characteristics and challenges as well as presents evaluation results confirming the hypothesis.

Detection and tracking are often treated as two separate problems. However, some tracking methods, e.g. template-based tracking methods, base their tracking on repeated specific detections. They learn a model of the object that is adaptively updated. That is, detection and tracking are performed jointly. The thesis includes a template-based tracking method designed specifically for thermal infrared imagery, describes a thermal infrared dataset for evaluation of template-based tracking methods, and provides an overview of the first challenge on short-term,single-object tracking in thermal infrared video. Finally, two applications employing detection and tracking methods are presented.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. p. 66
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1744
Keywords
thermal, infrared, detection, tracking
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-126955 (URN)10.3384/lic.diva-126955 (DOI)978-91-7685-789-2 (ISBN)
Presentation
2016-05-10, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 16:16 (English)
Opponent
Supervisors
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784EU, FP7, Seventh Framework Programme, 607567
Available from: 2016-04-11 Created: 2016-04-08 Last updated: 2019-10-29Bibliographically approved
2. Learning to Analyze what is Beyond the Visible Spectrum
Open this publication in new window or tab >>Learning to Analyze what is Beyond the Visible Spectrum
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing camera price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as there exists a measurable temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy.

This thesis addresses the problem of automatic image analysis in thermal infrared images with a focus on machine learning methods. The main purpose of this thesis is to study the variations of processing required due to the thermal infrared data modality. In particular, three different problems are addressed: visual object tracking, anomaly detection, and modality transfer. All these are research areas that have been and currently are subject to extensive research. Furthermore, they are all highly relevant for a number of different real-world applications.

The first addressed problem is visual object tracking, a problem for which no prior information other than the initial location of the object is given. The main contribution concerns benchmarking of short-term single-object (STSO) visual object tracking methods in thermal infrared images. The proposed dataset, LTIR (Linköping Thermal Infrared), was integrated in the VOT-TIR2015 challenge, introducing the first ever organized challenge on STSO tracking in thermal infrared video. Another contribution also related to benchmarking is a novel, recursive, method for semi-automatic annotation of multi-modal video sequences. Based on only a few initial annotations, a video object segmentation (VOS) method proposes segmentations for all remaining frames and difficult parts in need for additional manual annotation are automatically detected. The third contribution to the problem of visual object tracking is a template tracking method based on a non-parametric probability density model of the object's thermal radiation using channel representations.

The second addressed problem is anomaly detection, i.e., detection of rare objects or events. The main contribution is a method for truly unsupervised anomaly detection based on Generative Adversarial Networks (GANs). The method employs joint training of the generator and an observation to latent space encoder, enabling stratification of the latent space and, thus, also separation of normal and anomalous samples. The second contribution is the previously unaddressed problem of obstacle detection in front of moving trains using a train-mounted thermal camera. Adaptive correlation filters are updated continuously and missed detections of background are treated as detections of anomalies, or obstacles. The third contribution to the problem of anomaly detection is a method for characterization and classification of automatically detected district heat leakages for the purpose of false alarm reduction.

Finally, the thesis addresses the problem of modality transfer between thermal infrared and visual spectrum images, a previously unaddressed problem. The contribution is a method based on Convolutional Neural Networks (CNNs), enabling perceptually realistic transformations of thermal infrared to visual images. By careful design of the loss function the method becomes robust to image pair misalignments. The method exploits the lower acuity for color differences than for luminance possessed by the human visual system, separating the loss into a luminance and a chrominance part.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 94
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2024
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-161077 (URN)10.3384/diss.diva-161077 (DOI)9789179299811 (ISBN)
Public defence
2019-12-18, Ada Lovelace, B-huset, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Funder
Swedish Research Council, D0570301
Available from: 2019-11-13 Created: 2019-10-23 Last updated: 2019-11-19Bibliographically approved

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