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Larsen, M., Rolfsfjord, S., Gusland, D., Ahlberg, J. & Mathiassen, K. (2024). BASE: Probably a Better Approach to Visual Multi-Object Tracking. In: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Rome, Italy, 2024: . Paper presented at International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISAPP (pp. 110-121). SciTePress
Öppna denna publikation i ny flik eller fönster >>BASE: Probably a Better Approach to Visual Multi-Object Tracking
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2024 (Engelska)Ingår i: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Rome, Italy, 2024, SciTePress, 2024, s. 110-121Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The field of visual object tracking is dominated by methods that combine simple tracking algorithms and ad hoc schemes. Probabilistic tracking algorithms, which are leading in other fields, are surprisingly absent from the leaderboards. We found that accounting for distance in target kinematics, exploiting detector confidence and modelling non-uniform clutter characteristics is critical for a probabilistic tracker to work in visual tracking. Previous probabilistic methods fail to address most or all these aspects, which we believe is why they fall so far behind current state-of-the-art (SOTA) methods (there are no probabilistic trackers in the MOT17 top 100). To rekindle progress among probabilistic approaches, we propose a set of pragmatic models addressing these challenges, and demonstrate how they can be incorporated into a probabilistic framework. We present BASE (Bayesian Approximation Single-hypothesis Estimator), a simple, performant and easily extendible visual tracker, achieving state-of-the-art (SOTA) on MOT17 and MOT20, without using Re-Id. Code available at https://github.com/ffi-no/paper-base-visapp-2024.

Ort, förlag, år, upplaga, sidor
SciTePress, 2024
Serie
VISIGRAPP, ISSN 2184-4321
Nyckelord
Visual Multi-Object Tracking, Probabilistic Tracking, Distance-Aware, Association-Less Track Management
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-201409 (URN)10.5220/0012386600003660 (DOI)
Konferens
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISAPP
Tillgänglig från: 2024-03-07 Skapad: 2024-03-07 Senast uppdaterad: 2024-03-07
Bešenić, K., Ahlberg, J. & Pandžić, I. (2024). Let Me Take a Better Look: Towards Video-Based Age Estimation. In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM, Rome , Italy: . Paper presented at 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM (pp. 57-59).
Öppna denna publikation i ny flik eller fönster >>Let Me Take a Better Look: Towards Video-Based Age Estimation
2024 (Engelska)Ingår i: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM, Rome , Italy, 2024, s. 57-59Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Taking a better look at subjects of interest helps humans to improve confidence in their age estimation. Unlike still images, sequences offer spatio-temporal dynamic information that contains many cues related to age progression. A review of previous work on video-based age estimation indicates that this is an underexplored field of research. This may be caused by a lack of well-defined and publicly accessible video benchmark protocol, as well as the absence of video-oriented training data. To address the former issue, we propose a carefully designed video age estimation benchmark protocol and make it publicly available. To address the latter issue, we design a video-specific age estimation method that leverages pseudo-labeling and semi-supervised learning. Our results show that the proposed method outperforms image-based baselines on both offline and online benchmark protocols, while the online estimation stability is improved by more than 50%.

Serie
ICPRAM, ISSN 2184-4313
Nyckelord
Age, Video, Benchmark, Semi-Supervised, Pseudo-Labeling.
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-201405 (URN)10.5220/0012376800003654 (DOI)978-989-758-684-2 (ISBN)
Konferens
13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
Tillgänglig från: 2024-03-07 Skapad: 2024-03-07 Senast uppdaterad: 2024-03-22
Bešenić, K., Ahlberg, J. & Pandžić, I. S. (2023). Picking out the bad apples: unsupervised biometric data filtering for refined age estimation. The Visual Computer, 39, 219-237
Öppna denna publikation i ny flik eller fönster >>Picking out the bad apples: unsupervised biometric data filtering for refined age estimation
2023 (Engelska)Ingår i: The Visual Computer, ISSN 0178-2789, E-ISSN 1432-2315, Vol. 39, s. 219-237Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Introduction of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, facial image datasets with biometric trait labels are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts of weakly labeled and noisy data. This work is focused on picking out the bad apples from web-scraped facial datasets by automatically removing erroneous samples that impair their usability. The unsupervised facial biometric data filtering method presented in this work greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped datasets demonstrate the effectiveness of the proposed method with respect to real and apparent age estimation based on five different age estimation methods. Furthermore, we apply the proposed method, together with a newly devised strategy for merging multiple datasets, to data collected from three major web-based data sources (i.e., IMDb, Wikipedia, Google) and derive the new Biometrically Filtered Famous Figure Dataset or B3FD. The proposed dataset, which is made publicly available, enables considerable performance gains for all tested age estimation methods and age estimation tasks. This work highlights the importance of training data quality compared to data quantity and selection of the estimation method.

Ort, förlag, år, upplaga, sidor
Heidelberg, Germany: Springer, 2023
Nyckelord
Filtering, Biometric, Unsupervised, Web scraping, Age estimation, Dataset design
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-182685 (URN)10.1007/s00371-021-02323-y (DOI)000740610100001 ()2-s2.0-85122654875 (Scopus ID)
Anmärkning

Funding: The author K. Besenic receives Ph.D. scholarship from the company Visage Technologies.

Tillgänglig från: 2022-02-02 Skapad: 2022-02-02 Senast uppdaterad: 2023-05-15Bibliografiskt granskad
Gonzalez, S. A. R., Shimoni, M., Plaza, J., Plaza, A., Renhorn, I. & Ahlberg, J. (2020). The Detection of Concealed Targets in Woodland Areas using Hyperspectral Imagery. In: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS): . Paper presented at IEEE Latin American GRSS and ISPRS Remote Sensing Conference (LAGIRS), Santiago, CHILE, mar 21-26, 2020 (pp. 451-455). Santiago, Chile: IEEE
Öppna denna publikation i ny flik eller fönster >>The Detection of Concealed Targets in Woodland Areas using Hyperspectral Imagery
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2020 (Engelska)Ingår i: 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile: IEEE , 2020, s. 451-455Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Recent innovations in microelectronic and semiconductor technology enable the creation of smaller and economical hyperspectral cameras. A filter combined camera with advanced scanning module is a game changer that extends the application of miniature hyperspectral imagers to many security domains. This work presents the assessment of the imager L4 from Glana Sensors to detect concealed targets in woodland areas. Several target detection methods were applied to a collection of scenes acquired under various illumination conditions and containing different materials. The potential and limitations of this new imaging device in the context of difficult target detection in forested area are evaluated and discussed.

Ort, förlag, år, upplaga, sidor
Santiago, Chile: IEEE, 2020
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-169928 (URN)10.1109/LAGIRS48042.2020.9165611 (DOI)000626733300084 ()9781728143507 (ISBN)
Konferens
IEEE Latin American GRSS and ISPRS Remote Sensing Conference (LAGIRS), Santiago, CHILE, mar 21-26, 2020
Anmärkning

Funding Agencies|EOXPOSURE project under Horizon 2020 research and innovation program [734541]; Junta de Extremadura [Decreto 14/2018, GR18060]

Tillgänglig från: 2020-09-24 Skapad: 2020-09-24 Senast uppdaterad: 2021-06-11
Berg, A., Ahlberg, J. & Felsberg, M. (2020). Unsupervised Adversarial Learning of Anomaly Detection in the Wild. In: Giuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senén Barro, Alberto Bugarín, Jérôme Lang (Ed.), Proceedings of the 24th European Conference on Artificial Intelligence (ECAI): . Paper presented at 24th European Conference on Artificial Intelligence (ECAI) (pp. 1002-1008). Amsterdam: IOS Press, 325
Öppna denna publikation i ny flik eller fönster >>Unsupervised Adversarial Learning of Anomaly Detection in the Wild
2020 (Engelska)Ingår i: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI) / [ed] Giuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senén Barro, Alberto Bugarín, Jérôme Lang, Amsterdam: IOS Press, 2020, Vol. 325, s. 1002-1008Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Unsupervised learning of anomaly detection in high-dimensional data, such as images, is a challenging problem recently subject to intense research. Through careful modelling of the data distribution of normal samples, it is possible to detect deviant samples, so called anomalies. Generative Adversarial Networks (GANs) can model the highly complex, high-dimensional data distribution of normal image samples, and have shown to be a suitable approach to the problem. Previously published GAN-based anomaly detection methods often assume that anomaly-free data is available for training. However, this assumption is not valid in most real-life scenarios, a.k.a. in the wild. In this work, we evaluate the effects of anomaly contaminations in the training data on state-of-the-art GAN-based anomaly detection methods. As expected, detection performance deteriorates. To address this performance drop, we propose to add an additional encoder network already at training time and show that joint generator-encoder training stratifies the latent space, mitigating the problem with contaminated data. We show experimentally that the norm of a query image in this stratified latent space becomes a highly significant cue to discriminate anomalies from normal data. The proposed method achieves state-of-the-art performance on CIFAR-10 as well as on a large, previously untested dataset with cell images.

Ort, förlag, år, upplaga, sidor
Amsterdam: IOS Press, 2020
Serie
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314 ; 325
Nyckelord
anomaly detection, GANs, generative adversarial networks, deep learning
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-174310 (URN)10.3233/FAIA200194 (DOI)000650971301032 ()9781643681009 (ISBN)9781643681016 (ISBN)
Konferens
24th European Conference on Artificial Intelligence (ECAI)
Projekt
Learning Systems for Remote ThermographyEnergy Minimization for Computational CamerasELLIITAggregate FARming in the CLOUD (AFarCloud)
Forskningsfinansiär
Vetenskapsrådet, D0570301EU, Horisont 2020, 783221Vetenskapsrådet, 2014-6227ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Anmärkning

Funding: Swedish Research Council through the project Learning Systems for Remote Thermography [D0570301]; Swedish Research Council through project Energy Minimization for Computational Cameras [2014-6227]; Swedish Research Council through project ELLIIT (the Strategic Area for ICT research - Swedish Government); European Unions Horizon 2020 reseach and innovation programme [783221]

Tillgänglig från: 2021-03-24 Skapad: 2021-03-24 Senast uppdaterad: 2022-06-16Bibliografiskt granskad
Bešenić, K., Ahlberg, J. & Pandžić, I. (2019). Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2019): . Paper presented at 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), Prague, CZECH REPUBLIC, feb 25-27, 2019 (pp. 209-217). SciTePress, 5
Öppna denna publikation i ny flik eller fönster >>Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation
2019 (Engelska)Ingår i: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2019), SciTePress, 2019, Vol. 5, s. 209-217Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Availability of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, datasets with age and gender annotations are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts weakly labeled noisy data. The unsupervised facial biometric data filtering method presented in this paper greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped facial datasets demonstrate the effectiveness of the proposed method, with respect to training and validation scores, training convergence, and generalization capabilities of trained age and gender estimators.

Ort, förlag, år, upplaga, sidor
SciTePress, 2019
Nyckelord
Biometric; Web-Scraping; Age; Gender
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-154867 (URN)10.5220/0007257202090217 (DOI)000570349800021 ()978-989-758-354-4 (ISBN)
Konferens
14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), Prague, CZECH REPUBLIC, feb 25-27, 2019
Tillgänglig från: 2019-03-01 Skapad: 2019-03-01 Senast uppdaterad: 2022-05-04Bibliografiskt granskad
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)
Öppna denna publikation i ny flik eller fönster >>Generating Visible Spectrum Images from Thermal Infrared
2018 (Engelska)Ingår i: Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 1224-1233Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2018
Serie
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, E-ISSN 2160-7516
Nationell ämneskategori
Datorseende och robotik (autonoma system) Teknik och teknologier
Identifikatorer
urn:nbn:se:liu:diva-149429 (URN)10.1109/CVPRW.2018.00159 (DOI)000457636800152 ()9781538661000 (ISBN)9781538661017 (ISBN)
Konferens
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 8-22 June 2018, Salt Lake City, UT, USA
Forskningsfinansiär
Vetenskapsrådet, 2013-5703Vetenskapsrådet, 2014-6227
Anmärkning

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

Tillgänglig från: 2018-06-29 Skapad: 2018-06-29 Senast uppdaterad: 2020-02-03Bibliografiskt granskad
Markus, N., Gogic, I., Pandžic, I. & Ahlberg, J. (2018). Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment. In: Proceedings of BMVC 2018 and Workshops: . Paper presented at British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, UK, 3-6 September 2018 (pp. 1-11). Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition, Article ID 896.
Öppna denna publikation i ny flik eller fönster >>Memory-efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
2018 (Engelska)Ingår i: Proceedings of BMVC 2018 and Workshops, Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition , 2018, s. 1-11, artikel-id 896Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Ren et al. [17] recently introduced a method for aggregating multiple decision trees into a strong predictor by interpreting a path taken by a sample down each tree as a binary vector and performing linear regression on top of these vectors stacked together. They provided experimental evidence that the method offers advantages over the usual approaches for combining decision trees (random forests and boosting). The method truly shines when the regression target is a large vector with correlated dimensions, such as a 2D face shape represented with the positions of several facial landmarks. However, we argue that their basic method is not applicable in many practical scenarios due to large memory requirements. This paper shows how this issue can be solved through the use of quantization and architectural changes of the predictor that maps decision tree-derived encodings to the desired output.

Ort, förlag, år, upplaga, sidor
Newcastle upon Tyne, UK: The British Machine Vision Association and Society for Pattern Recognition, 2018
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-152550 (URN)
Konferens
British Machine Vision Conference (BMVC), Northumbria University, Newcastle upon Tyne, UK, 3-6 September 2018
Tillgänglig från: 2018-11-06 Skapad: 2018-11-06 Senast uppdaterad: 2019-08-22Bibliografiskt granskad
Hatami, S., Dahl-Jendelin, A., Ahlberg, J. & Nelsson, C. (2018). Selective Laser Melting Process Monitoring by Means of Thermography. In: Proceedings of Euro Powder Metallurgy Congress (Euro PM): . Paper presented at Euro Powder Metallurgy Congress (Euro PM). European Powder Metallurgy Association (EPMA), Article ID 3957771.
Öppna denna publikation i ny flik eller fönster >>Selective Laser Melting Process Monitoring by Means of Thermography
2018 (Engelska)Ingår i: Proceedings of Euro Powder Metallurgy Congress (Euro PM), European Powder Metallurgy Association (EPMA) , 2018, artikel-id 3957771Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Selective laser melting (SLM) enables production of highly intricate components. From this point of view, the capabilities of this technology are known to the industry and have been demonstrated in numerous applications. Nonetheless, for serial production purposes the manufacturing industry has so far been reluctant in substituting its conventional methods with SLM. One underlying reason is the lack of simple and reliable process monitoring methods. This study examines the feasibility of using thermography for process monitoring. To this end, an infra-red (IR) camera was mounted off-axis to monitor and record the temperature of every layer. The recorded temperature curves are analysed and interpreted with respect to different stages of the process. Furthermore, the possibility of detecting variations in laser settings by means of thermography is demonstrated. The results show that once thermal patterns are identified, this data can be utilized for in-process and post-process monitoring of SLM production.

Ort, förlag, år, upplaga, sidor
European Powder Metallurgy Association (EPMA), 2018
Nationell ämneskategori
Metallurgi och metalliska material
Identifikatorer
urn:nbn:se:liu:diva-152570 (URN)978-1-899072-50-7 (ISBN)
Konferens
Euro Powder Metallurgy Congress (Euro PM)
Forskningsfinansiär
VINNOVA, 2016-04486
Tillgänglig från: 2018-11-07 Skapad: 2018-11-07 Senast uppdaterad: 2018-11-21
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.
Öppna denna publikation i ny flik eller fönster >>Effective evaluation of privacy protection techniques in visible and thermal imagery
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2017 (Engelska)Ingår i: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 26, nr 5, artikel-id 051408Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
SPIE - International Society for Optical Engineering, 2017
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-140495 (URN)10.1117/1.JEI.26.5.051408 (DOI)000414251400009 ()
Forskningsfinansiär
Vetenskapsrådet, D0570301EU, FP7, Sjunde ramprogrammet, 312784
Anmärkning

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

Tillgänglig från: 2017-09-05 Skapad: 2017-09-05 Senast uppdaterad: 2018-01-13Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-6763-5487

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