Human Detection Based on Infrared Images in Forestry Environments
2016 (English)In: Image Analysis and Recognition: 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings, 2016, 175-182 p.Conference paper (Refereed)
It is essential to have a reliable system to detect humans in close range of forestry machines to stop cutting or carrying operations to prohibit any harm to humans. Due to the lighting conditions and high occlusion from the vegetation, human detection using RGB cameras is difficult. This paper introduces two human detection methods in forestry environments using a thermal camera; one shape-dependent and one shape-independent approach. Our segmentation algorithm estimates location of the human by extracting vertical and horizontal borders of regions of interest (ROIs). Based on segmentation results, features such as ratio of height to width and location of the hottest spot are extracted for the shape-dependent method. For the shape-independent method all extracted ROI are resized to the same size, then the pixel values (temperatures) are used as a set of features. The features from both methods are fed into different classifiers and the results are evaluated using side-accuracy and side-efficiency. The results show that by using shape-independent features, based on three consecutive frames, we reach a precision rate of 80 % and recall of 76 %.
Place, publisher, year, edition, pages
2016. 175-182 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 9730
Human detection, Thermal images, Shape-dependent, Shape-independent, Side-accuracy, Side-efficiency
IdentifiersURN: urn:nbn:se:umu:diva-124428DOI: 10.1007/978-3-319-41501-7_20ISBN: 978-3-319-41501-7OAI: oai:DiVA.org:umu-124428DiVA: diva2:951913
13th International Conference on Image Analysis and Recognition, ICIAR 2016, Póvoa de Varzim, Portugal, July 13-15