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Detection and Tracking in 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
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. , 66 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1744
Keyword [en]
thermal, infrared, detection, tracking
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-126955DOI: 10.3384/lic.diva-126955ISBN: 978-91-7685-789-2 (print)OAI: oai:DiVA.org:liu-126955DiVA: diva2:918038
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: 2017-02-13Bibliographically approved
List of papers
1. A Thermal Object Tracking Benchmark
Open this publication in new window or tab >>A Thermal Object Tracking Benchmark
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Short-term single-object (STSO) tracking in thermal images is a challenging problem relevant in a growing number of applications. In order to evaluate STSO tracking algorithms on visual imagery, there are de facto standard benchmarks. However, we argue that tracking in thermal imagery is different than in visual imagery, and that a separate benchmark is needed. The available thermal infrared datasets are few and the existing ones are not challenging for modern tracking algorithms. Therefore, we hereby propose a thermal infrared benchmark according to the Visual Object Tracking (VOT) protocol for evaluation of STSO tracking methods. The benchmark includes the new LTIR dataset containing 20 thermal image sequences which have been collected from multiple sources and annotated in the format used in the VOT Challenge. In addition, we show that the ranking of different tracking principles differ between the visual and thermal benchmarks, confirming the need for the new benchmark.

Place, publisher, year, edition, pages
IEEE, 2015
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-121001 (URN)10.1109/AVSS.2015.7301772 (DOI)000380619700052 ()978-1-4673-7632-7 (ISBN)
Conference
12th IEEE International Conference on Advanced Video- and Signal-based Surveillance, Karlsruhe, Germany, August 25-28 2015
Available from: 2015-09-02 Created: 2015-09-02 Last updated: 2016-12-08Bibliographically approved
2. The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
Open this publication in new window or tab >>The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
Show others...
2015 (English)In: Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers (IEEE), 2015, 639-651 p.Conference paper, Published paper (Refereed)
Abstract [en]

The Thermal Infrared Visual Object Tracking challenge 2015, VOTTIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply prelearned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015
Series
IEEE International Conference on Computer Vision. Proceedings, ISSN 1550-5499
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-126917 (URN)10.1109/ICCVW.2015.86 (DOI)000380434700077 ()978-146738390-5 (ISBN)
External cooperation:
Conference
IEEE International Conference on Computer Vision Workshop (ICCVW. 7-13 Dec. 2015 Santiago, Chile
Available from: 2016-04-07 Created: 2016-04-07 Last updated: 2016-08-26Bibliographically approved
3. Channel Coded Distribution Field Tracking for Thermal Infrared Imagery
Open this publication in new window or tab >>Channel Coded Distribution Field Tracking for Thermal Infrared Imagery
2016 (English)In: PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), IEEE , 2016, 1248-1256 p.Conference 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
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:nbn:se:liu:diva-134402 (URN)10.1109/CVPRW.2016.158 (DOI)000391572100151 ()978-1-5090-1438-5 (ISBN)978-1-5090-1437-8 (ISBN)
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, 607567
Available from: 2017-02-09 Created: 2017-02-09 Last updated: 2017-02-15
4. Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera
Open this publication in new window or tab >>Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera
2015 (English)In: Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings / [ed] Rasmus R. Paulsen; Kim S. Pedersen, Springer, 2015, 492-503 p.Conference paper, Published paper (Refereed)
Abstract [en]

We propose a method for detecting obstacles on the railway in front of a moving train using a monocular thermal camera. The problem is motivated by the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. The proposed method includes a novel way of detecting the rails in the imagery, as well as a way to detect anomalies on the railway. While the problem at a first glance looks similar to road and lane detection, which in the past has been a popular research topic, a closer look reveals that the problem at hand is previously unaddressed. As a consequence, relevant datasets are missing as well, and thus our contribution is two-fold: We propose an approach to the novel problem of obstacle detection on railways and we describe the acquisition of a novel data set.

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 9127
Keyword
Thermal imaging; Computer vision; Train safety; Railway detection; Anomaly detection; Obstacle detection
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-119507 (URN)10.1007/978-3-319-19665-7_42 (DOI)978-3-319-19664-0 (ISBN)978-3-319-19665-7 (ISBN)
Conference
19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015
Available from: 2015-06-22 Created: 2015-06-18 Last updated: 2016-06-09Bibliographically approved
5. Enhanced analysis of thermographic images for monitoring of district heat pipe networks
Open this publication in new window or tab >>Enhanced analysis of thermographic images for monitoring of district heat pipe networks
2016 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 83, no 2, 215-223 p.Article in journal (Refereed) Published
Abstract [en]

We address two problems related to large-scale aerial monitoring of district heating networks. First, we propose a classification scheme to reduce the number of false alarms among automatically detected leakages in district heating networks. The leakages are detected in images captured by an airborne thermal camera, and each detection corresponds to an image region with abnormally high temperature. This approach yields a significant number of false positives, and we propose to reduce this number in two steps; by (a) using a building segmentation scheme in order to remove detections on buildings, and (b) to use a machine learning approach to classify the remaining detections as true or false leakages. We provide extensive experimental analysis on real-world data, showing that this post-processing step significantly improves the usefulness of the system. Second, we propose a method for characterization of leakages over time, i.e., repeating the image acquisition one or a few years later and indicate areas that suffer from an increased energy loss. We address the problem of finding trends in the degradation of pipe networks in order to plan for long-term maintenance, and propose a visualization scheme exploiting the consecutive data collections. (C) 2016 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2016
Keyword
Remote thermography; Classification; Pattern recognition; District heating; Thermal infrared
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-133004 (URN)10.1016/j.patrec.2016.07.002 (DOI)000386874800013 ()
Note

Funding Agencies|Swedish Research Council (Vetenskapsradet) through project Learning systems for remote thermography [621-2013-5703]; Swedish Research Council [2014-6227]

Available from: 2016-12-08 Created: 2016-12-07 Last updated: 2017-11-29

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