liu.seSearch for publications in DiVA
Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 15) Show all publications
Berg, A., Ahlberg, J. & Felsberg, M. (2018). Generating Visible Spectrum Images from Thermal Infrared. In: Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018: . Paper presented at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 8-22 June 2018, Salt Lake City, UT, USA (pp. 1224-1233). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Generating Visible Spectrum Images from Thermal Infrared
2018 (English)In: Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1224-1233Conference paper, Published paper (Refereed)
Abstract [en]

Transformation of thermal infrared (TIR) images into visual, i.e. perceptually realistic color (RGB) images, is a challenging problem. TIR cameras have the ability to see in scenarios where vision is severely impaired, for example in total darkness or fog, and they are commonly used, e.g., for surveillance and automotive applications. However, interpretation of TIR images is difficult, especially for untrained operators. Enhancing the TIR image display by transforming it into a plausible, visual, perceptually realistic RGB image presumably facilitates interpretation. Existing grayscale to RGB, so called, colorization methods cannot be applied to TIR images directly since those methods only estimate the chrominance and not the luminance. In the absence of conventional colorization methods, we propose two fully automatic TIR to visual color image transformation methods, a two-step and an integrated approach, based on Convolutional Neural Networks. The methods require neither pre- nor postprocessing, do not require any user input, and are robust to image pair misalignments. We show that the methods do indeed produce perceptually realistic results on publicly available data, which is assessed both qualitatively and quantitatively.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, E-ISSN 2160-7516
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-149429 (URN)10.1109/CVPRW.2018.00159 (DOI)000457636800152 ()9781538661000 (ISBN)9781538661017 (ISBN)
Conference
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 8-22 June 2018, Salt Lake City, UT, USA
Funder
Swedish Research Council, 2013-5703Swedish Research Council, 2014-6227
Note

Print on Demand(PoD) ISSN: 2160-7508.

Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2019-06-19Bibliographically approved
Nawaz, T., Berg, A., Ferryman, J., Ahlberg, J. & Felsberg, M. (2017). Effective evaluation of privacy protection techniques in visible and thermal imagery. Journal of Electronic Imaging (JEI), 26(5), Article ID 051408.
Open this publication in new window or tab >>Effective evaluation of privacy protection techniques in visible and thermal imagery
Show others...
2017 (English)In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 26, no 5, article id 051408Article in journal (Refereed) Published
Abstract [en]

Privacy protection may be defined as replacing the original content in an image region with a new (less intrusive) content having modified target appearance information to make it less recognizable by applying a privacy protection technique. Indeed the development of privacy protection techniques needs also to be complemented with an established objective evaluation method to facilitate their assessment and comparison. Generally, existing evaluation methods rely on the use of subjective judgements or assume a specific target type in image data and use target detection and recognition accuracies to assess privacy protection. This work proposes a new annotation-free evaluation method that is neither subjective nor assumes a specific target type. It assesses two key aspects of privacy protection: protection and utility. Protection is quantified as an appearance similarity and utility is measured as a structural similarity between original and privacy-protected image regions. We performed an extensive experimentation using six challenging datasets (having 12 video sequences) including a new dataset (having six sequences) that contains visible and thermal imagery. The new dataset, called TST-Priv, is made available online below for community. We demonstrate effectiveness of the proposed method by evaluating six image-based privacy protection techniques, and also show comparisons of the proposed method over existing methods.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2017
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-140495 (URN)10.1117/1.JEI.26.5.051408 (DOI)000414251400009 ()
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784
Note

Funding agencies:  Swedish Research Council through the project Learning Systems for Remote Thermography [D0570301]; European Community [312784]

Available from: 2017-09-05 Created: 2017-09-05 Last updated: 2018-01-13Bibliographically approved
Berg, A., Ahlberg, J. & Felsberg, M. (2017). Object Tracking in Thermal Infrared Imagery based on Channel Coded Distribution Fields. In: : . Paper presented at Swedish Symposium on Image Analysis. Svenska sällskapet för automatiserad bildanalys (SSBA)
Open this publication in new window or tab >>Object Tracking in Thermal Infrared Imagery based on Channel Coded Distribution Fields
2017 (English)Conference paper, Oral presentation only (Other academic)
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. Tracking methods designed 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 templatebased tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. 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. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

Place, publisher, year, edition, pages
Svenska sällskapet för automatiserad bildanalys (SSBA), 2017
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-136743 (URN)
Conference
Swedish Symposium on Image Analysis
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784EU, FP7, Seventh Framework Programme, 607567Swedish Research Council, 2014-6227
Available from: 2017-04-24 Created: 2017-04-24 Last updated: 2019-05-09Bibliographically approved
Berg, A., Felsberg, M., Häger, G. & Ahlberg, J. (2016). An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge. In: : . Paper presented at Swedish Symposium on Image Analysis.
Open this publication in new window or tab >>An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge
2016 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

The Thermal Infrared Visual Object Tracking (VOT-TIR2015) Challenge was organized in conjunction with ICCV2015. It was the first benchmark on short-term,single-target tracking in thermal infrared (TIR) sequences. The challenge aimed at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. It was based on the VOT2013 Challenge, but introduced the following novelties: (i) the utilization of the LTIR (Linköping TIR) dataset, (ii) adaption of the VOT2013 attributes to thermal data, (iii) a similar evaluation to that of VOT2015. This paper provides an overview of the VOT-TIR2015 Challenge as well as the results of the 24 participating trackers.

Series
Svenska sällskapet för automatiserad bildanalys (SSBA)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-127598 (URN)
Conference
Swedish Symposium on Image Analysis
Available from: 2016-05-03 Created: 2016-05-03 Last updated: 2018-01-10Bibliographically approved
Berg, A. (2016). Detection and Tracking in Thermal Infrared Imagery. (Licentiate dissertation). Linköping: Linköping University Electronic Press
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: 2018-01-10Bibliographically approved
Felsberg, M., Kristan, M., Matas, J., Leonardis, A., Pflugfelder, R., Häger, G., . . . He, Z. (2016). The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results. In: Hua G., Jégou H. (Ed.), Computer Vision – ECCV 2016 Workshops. ECCV 2016.: . Paper presented at 14th European Conference on Computer Vision (ECCV) (pp. 824-849). SPRINGER INT PUBLISHING AG
Open this publication in new window or tab >>The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
Show others...
2016 (English)In: Computer Vision – ECCV 2016 Workshops. ECCV 2016. / [ed] Hua G., Jégou H., SPRINGER INT PUBLISHING AG , 2016, p. 824-849Conference paper, Published paper (Refereed)
Abstract [en]

The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second 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-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

Place, publisher, year, edition, pages
SPRINGER INT PUBLISHING AG, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9914
Keywords
Performance evaluation; Object tracking; Thermal IR; VOT
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-133773 (URN)10.1007/978-3-319-48881-3_55 (DOI)000389501700055 ()978-3-319-48881-3 (ISBN)978-3-319-48880-6 (ISBN)
Conference
14th European Conference on Computer Vision (ECCV)
Available from: 2017-01-11 Created: 2017-01-09 Last updated: 2018-10-15
Berg, A., Ahlberg, J. & Felsberg, M. (2015). A thermal infrared dataset for evaluation of short-term tracking methods. In: : . Paper presented at Swedish Symposium on Image Analysis.
Open this publication in new window or tab >>A thermal infrared dataset for evaluation of short-term tracking methods
2015 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

During recent years, thermal cameras have decreased in both size and cost while improving image quality. The area of use for such cameras has expanded with many exciting applications, many of which require tracking of objects. While being subject to extensive research in the visual domain, tracking in thermal imagery has historically been of interest mainly for military purposes. The available thermal infrared datasets for evaluating methods addressing these problems are few and the ones that do are not challenging enough for today’s tracking algorithms. Therefore, we hereby propose a thermal infrared dataset for evaluation of short-term tracking methods. The dataset consists of 20 sequences which have been collected from multiple sources and the data format used is in accordance with the Visual Object Tracking (VOT) Challenge.

Series
Svenska sällskapet för automatiserad bildanalys (SSBA)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-127541 (URN)
Conference
Swedish Symposium on Image Analysis
Available from: 2016-05-03 Created: 2016-05-03 Last updated: 2018-01-10Bibliographically approved
Berg, A., Ahlberg, J. & Felsberg, M. (2015). A Thermal Object Tracking Benchmark. In: : . Paper presented at 12th IEEE International Conference on Advanced Video- and Signal-based Surveillance, Karlsruhe, Germany, August 25-28 2015. IEEE
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: 2018-01-11Bibliographically approved
Berg, A., Öfjäll, K., Ahlberg, J. & Felsberg, M. (2015). Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera. In: Rasmus R. Paulsen; Kim S. Pedersen (Ed.), Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings. Paper presented at 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015 (pp. 492-503). Springer
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, p. 492-503Conference 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, E-ISSN 1611-3349 ; 9127
Keywords
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: 2018-02-07Bibliographically approved
Ahlberg, J. & Berg, A. (2015). Evaluating Template Rescaling in Short-Term Single-Object Tracking. In: : . Paper presented at 17th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Karlsruhe, Germany, August 25, 2015. IEEE
Open this publication in new window or tab >>Evaluating Template Rescaling in Short-Term Single-Object Tracking
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, short-term single-object tracking has emerged has a popular research topic, as it constitutes the core of more general tracking systems. Many such tracking methods are based on matching a part of the image with a template that is learnt online and represented by, for example, a correlation filter or a distribution field. In order for such a tracker to be able to not only find the position, but also the scale, of the tracked object in the next frame, some kind of scale estimation step is needed. This step is sometimes separate from the position estimation step, but is nevertheless jointly evaluated in de facto benchmarks. However, for practical as well as scientific reasons, the scale estimation step should be evaluated separately – for example,theremightincertainsituationsbeothermethodsmore suitable for the task. In this paper, we describe an evaluation method for scale estimation in template-based short-term single-object tracking, and evaluate two state-of-the-art tracking methods where estimation of scale and position are separable.

Place, publisher, year, edition, pages
IEEE, 2015
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-121356 (URN)10.1109/AVSS.2015.7301745 (DOI)000380619700025 ()9781467376327 (ISBN)
Conference
17th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Karlsruhe, Germany, August 25, 2015
Funder
Swedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784
Available from: 2015-09-15 Created: 2015-09-15 Last updated: 2018-01-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6591-9400

Search in DiVA

Show all publications