A Prototype Device for Concealed Weapon Detection Using IR and CMOS Cameras Fast Image Fusion

Jędrasiak, Karol; Nawrat, Aleksander; Daniec, Krzysztof; Koteras, Roman; Mikulski, Michał; Grzejszczak, Tomasz

doi:10.1007/978-3-642-33564-8_51

Cited by 22 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SETh tracking method was compared with five state-of-the-art methods and achieved comparable or superior results, suggesting that it is possible to apply it in real-life applications e.g. visual-based control of UAVs [37][38][39][40] or tracking pedestrians [41][42][43].…”

Section: Testsmentioning

confidence: 97%

An Evaluation of SETh—The Method for Long-Term Feature Tracking

Jędrasiak

Nawrat

2015

Studies in Systems, Decision and Control

Self Cite

View full text Add to dashboard Cite

The chapter presents a novel long-term object tracking method called SETh. It is an adaptive tracking by detection method which allows near real-time tracking within challenging sequences. The algorithm consists of three stages: detection, verification and learning. In order to measure the performance of the method a video data set consisting of more than a hundred videos was created and manually labelled by a human. Quality of the tracking by SETh was compared against five state-of-the-art methods. The presented method achieved results comparable and mostly exceeding the existing methods, which proves its capability for real life applications like e.g. vision-based control of UAVs.

show abstract

Section: Testsmentioning

confidence: 97%

An Evaluation of SETh—The Method for Long-Term Feature Tracking

Jędrasiak

Nawrat

2015

Studies in Systems, Decision and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…The images fusion task appears when one has to solve problems which one encounters when we use image processing systems in such applications as: image recognition [9,10,11,12], an unmanned aerial vehicle [13,14] used for image acquisition [15,16,17,18], object tracking [19,20,21] or images fusion [22,23].…”

Section: Introductionmentioning

confidence: 99%

Dynamical Pattern Vector in Pattern Recognition with the Use of Thermal Images

Kuś

2016

MATEC Web of Conferences

View full text Add to dashboard Cite

Abstract. The goal of the following paper was to develop the methodology of object tracking in adverse conditions. Suddenly appearing clouds, fog or smoke could be the examples of atmospheric conditions. We used thermal and visible images in each moment during object tracking. We computed the pattern vectors of the tracked object on the basis of the visual and thermal images separately. The pattern vector and current feature vector for an image of a given type are used to compute the distance between the object pattern vector and feature vector calculated for a given location of the aperture. It is calculated for both: the visual and thermal image. The crux of the proposed method was the algorithm of selection which distance (for visual or thermal image) was used for object tracking. It was obtained by multiplying the values of the distances between a pattern vector and current feature vector by some coefficients (different for thermal and visual images). The values of these coefficients depended on the usefulness of a given type of an image for pattern recognition. This usefulness was defined by the variability of the particular pixels in the image which is represented by calculating gradient in the image. On top of that, this study presented the examples of the object recognition by means of the developed method.

show abstract

“…The images fusion task constitute one of the problems which appear when we use image processing systems in such applications as: image recognition [10,11,12] an unmanned aerial vehicle [13,14] used for image acquisition [15,16,17,18], object tracking [19,20,21] or images fusion [22].…”

Section: Introductionmentioning

confidence: 99%

The Fusion of the Visual and Thermal Images on the Basis of Determining the Image Fragments which Contain Essential Details

Kuś

2016

MATEC Web of Conferences

View full text Add to dashboard Cite

Abstract. The aim of the following study was to develop a procedure which guarantees the data fusion of thermal and visual images. The first stage of the proposed algorithm consisted of images acquisition which guaranteed that the same parts of images represented the same parts of the observed terrain. The second stage depended on previous information about the searched object features. Two different situations were considered herein. In the case when we had the searched object's feature vector for both representations of a searched object, we could conduct the pattern recognition for each image. It was conducted separately for visual and thermal images. In this way, we obtained the important parts of the images which should be represented in a fused image. The other case examined in the paper, considered the situation in which we did not have the formalised information about the object. In this case, it was necessary to analyse whole images in order to define the potential parts of the images where the object could be found. This analysis should be helpful for an operator to indicate the parts of the images where there are some artefacts which can be the elements of the searched object. Therefore, in this case, the second stage of the algorithm consisted in calculating the local features of the images. These features constituted grey scale gradient computed for the pixels inside the aperture. This study presented the examples of the fused images obtained by means of the developed method.

show abstract

A Prototype Device for Concealed Weapon Detection Using IR and CMOS Cameras Fast Image Fusion

Cited by 22 publications

References 14 publications

An Evaluation of SETh—The Method for Long-Term Feature Tracking

An Evaluation of SETh—The Method for Long-Term Feature Tracking

Dynamical Pattern Vector in Pattern Recognition with the Use of Thermal Images

The Fusion of the Visual and Thermal Images on the Basis of Determining the Image Fragments which Contain Essential Details

Contact Info

Product

Resources

About