Detection and Tracking in Thermal Infrared Imagery
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
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.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1744
thermal, infrared, detection, tracking
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: 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
2016-05-10, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 16:16 (English)
Moeslund, Thomas B., Professor
Felsberg, Michael, ProfessorAhlberg, Jörgen, Adj universitetslektor
FunderSwedish Research Council, D0570301EU, FP7, Seventh Framework Programme, 312784EU, FP7, Seventh Framework Programme, 607567
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