Passive infrared thermographic imaging for mobile robot object identification

Fehlman, William L.; Hinders, Mark K.

doi:10.1002/rob.20307

Cited by 7 publications

(6 citation statements)

References 64 publications

(63 reference statements)

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“…Fehlman and Hinders [25][26] developed a classification system of outdoor non-radiating objects using thermal imagery for the purpose of a mobile robot navigation. The "extended" object classes were objects that extend laterally beyond the field of view of the camera and included brick walls, picket fences, hedges, and wooden walls.…”

Section: Image Featuresmentioning

confidence: 99%

“…Fehlman and Hinders [25][26] conducted an extensive search for the most favorable feature set and totaled over 290,000 combinations reaching up to 18 dimensions. The favorable feature subsets differed for each of the three evaluated classifiers.…”

Section: Classifiersmentioning

confidence: 99%

“…Many of the sources found used synthetic visual images which often yields better classification results in comparison to real imagery. In the case of thermal imagery, many of the sources found were based on outdoor environment [25][26] [36] which significantly changes the thermal signature of the objects in comparison to an indoor setting and makes it difficult to compare the recognition rates. In the case of combined visual and thermal dataset, one publication [37] used the visual band imagery for daytime recognition while the thermal imagery was used for night time but the two were not combined in any way.…”

Section: Summary Of Literature Reviewmentioning

confidence: 99%

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Multispectral image analysis for object recognition and classification

2016

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Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate some form of decision-making process. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various field including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance.The fundamental objectives of this research project were to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. The goal was not to find a new way to "fuse" the visual and thermal images together but rather establish a methodology to extract multispectral descriptors in order to improve a machine vision system's ability to recognize specific classes of objects.A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.Commonly used performance metrics were applied to assess the sensitivity, specificity and accuracy of each classifier.The research demonstrated that the highest recognition rate was achieved by an expert system (multiple classifiers) that combined the expertise of the visual-only classifier, the thermal-only classifier and the combined visual-thermal classifier.iii Acknowledgment

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Section: Image Featuresmentioning

confidence: 99%

Section: Classifiersmentioning

confidence: 99%

Section: Summary Of Literature Reviewmentioning

confidence: 99%

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Multispectral image analysis for object recognition and classification

2016

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“…This is due to the much wider infrared spectrum, compared with visible light. Another difficulty is that the sensitivity curve of an uncooled microbolometer sensor changes very quickly with minimum changes of its temperature [17]. To overcome these challenges, we proposed in [6] a contrast invariant descriptor for pedestrian classification in FIR images called HOPE.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

Part based pedestrian detection based on Logic inference

Olmeda

Armingol

Escalera

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Part based Pedestrian Detection Based on Logic InferenceDaniel Olmeda, Jose Maria Armingol and Arturo de la Escalera Abstract-This paper presents an approach on detection of largely occluded pedestrians. From a pair of synchronized cameras in the Visible Light (VL) and Far Infrared (FIR) spectrum individual detections are combined and final confidence is inferred using a small set of logic rules via a Markov Logic Network. Pedestrians not entirely contained in the image or occluded are detected based on the binary classification on subparts of the detection window. The presented method is applied to a pedestrian classification problem in urban environments. The classifier has been tested in an Intelligent Transportation System (ITS) platform as part of an Advanced Driver Assistance Systems (ADAS).

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“…For instance, in [ 6 ] a set of commercial passive sensors was evaluated to determine strengths and weaknesses applied to imaging regions for mud detection. In [ 7 ], analysis and preprocessing of thermal–physical features extracted from passive thermal objects were reported. Unlike our approach, we set up a low-cost IR camera, and deduced its measurement models as exact solutions.…”

Section: Introductionmentioning

confidence: 99%

Thermal Image Sensing Model for Robotic Planning and Search

Jiménez

Martínez‐García

2016

Sensors

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This work presents a search planning system for a rolling robot to find a source of infra-red (IR) radiation at an unknown location. Heat emissions are observed by a low-cost home-made IR passive visual sensor. The sensor capability for detection of radiation spectra was experimentally characterized. The sensor data were modeled by an exponential model to estimate the distance as a function of the IR image’s intensity, and, a polynomial model to estimate temperature as a function of IR intensities. Both theoretical models are combined to deduce a subtle nonlinear exact solution via distance-temperature. A planning system obtains feed back from the IR camera (position, intensity, and temperature) to lead the robot to find the heat source. The planner is a system of nonlinear equations recursively solved by a Newton-based approach to estimate the IR-source in global coordinates. The planning system assists an autonomous navigation control in order to reach the goal and avoid collisions. Trigonometric partial differential equations were established to control the robot’s course towards the heat emission. A sine function produces attractive accelerations toward the IR source. A cosine function produces repulsive accelerations against the obstacles observed by an RGB-D sensor. Simulations and real experiments of complex indoor are presented to illustrate the convenience and efficacy of the proposed approach.

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Passive infrared thermographic imaging for mobile robot object identification

Cited by 7 publications

References 64 publications

Multispectral image analysis for object recognition and classification

Multispectral image analysis for object recognition and classification

Part based pedestrian detection based on Logic inference

Thermal Image Sensing Model for Robotic Planning and Search

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