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Human Detection Based on Infrared Images in Forestry Environments
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Robotics)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Robotics)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Robotics)
2016 (engelsk)Inngår i: Image Analysis and Recognition (ICIAR 2016): 13th International Conference, ICIAR 2016, in Memory of Mohamed Kamel, Póvoa de Varzim, Portugal, July 13-15, 2016, Proceedings, 2016, s. 175-182Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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 %.

sted, utgiver, år, opplag, sider
2016. s. 175-182
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9730
Emneord [en]
Human detection, Thermal images, Shape-dependent, Shape-independent, Side-accuracy, Side-efficiency
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-124428DOI: 10.1007/978-3-319-41501-7_20ISI: 000386604000020ISBN: 978-3-319-41501-7 (digital)ISBN: 978-3-319-41500-0 (tryckt)OAI: oai:DiVA.org:umu-124428DiVA, id: diva2:951913
Konferanse
13th International Conference on Image Analysis and Recognition, ICIAR 2016, July 13-15, 2016, Póvoa de Varzim, Portugal
Tilgjengelig fra: 2016-08-10 Laget: 2016-08-10 Sist oppdatert: 2019-11-11bibliografisk kontrollert
Inngår i avhandling
1. Object Detection and Recognition in Unstructured Outdoor Environments
Åpne denne publikasjonen i ny fane eller vindu >>Object Detection and Recognition in Unstructured Outdoor Environments
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Computer vision and machine learning based systems are often developed to replace humans in harsh, dangerous, or tedious situations, as well as to reduce the required time to accomplish a task. Another goal is to increase performance by introducing automation to tasks such as inspections in manufacturing applications, sorting timber during harvesting, surveillance, fruit grading, yield prediction, and harvesting operations.Depending on the task, a variety of object detection and recognition algorithms can be applied, including both conventional and deep learning based approaches. Moreover, within the process of developing image analysis algorithms, it is essential to consider environmental challenges, e.g. illumination changes, occlusion, shadows, and divergence in colour, shape, texture, and size of objects.

The goal of this thesis is to address these challenges to support development of autonomous agricultural and forestry systems with enhanced performance and reduced need for human involvement.This thesis provides algorithms and techniques based on adaptive image segmentation for tree detection in forest environment and also yellow pepper recognition in greenhouses. For segmentation, seed point generation and a region growing method was used to detect trees. An algorithm based on reinforcement learning was developed to detect yellow peppers. RGB and depth data was integrated and used in classifiers to detect trees, bushes, stones, and humans in forest environments. Another part of the thesis describe deep learning based approaches to detect stumps and classify the level of rot based on images.

Another major contribution of this thesis is a method using infrared images to detect humans in forest environments. To detect humans, one shape-dependent and one shape-independent method were proposed.

Algorithms to recognize the intention of humans based on hand gestures were also developed. 3D hand gestures were recognized by first detecting and tracking hands in a sequence of depth images, and then utilizing optical flow constraint equations.

The thesis also presents methods to answer human queries about objects and their spatial relation in images. The solution was developed by merging a deep learning based method for object detection and recognition with natural language processing techniques.

sted, utgiver, år, opplag, sider
Umeå: Umeå University, 2019. s. 88
Serie
Report / UMINF, ISSN 0348-0542 ; 19.08
Emneord
Computer vision, Deep Learning, Harvesting Robots, Automatic Detection and Recognition
HSV kategori
Forskningsprogram
datalogi
Identifikatorer
urn:nbn:se:umu:diva-165069 (URN)978-91-7855-147-7 (ISBN)
Disputas
2019-12-05, MA121, MIT Building, Umeå, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-11-14 Laget: 2019-11-08 Sist oppdatert: 2019-11-12bibliografisk kontrollert

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