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dc.contributor.authorKhemmar, Redouane
dc.contributor.authorGouveia, Matthias
dc.contributor.authorDecoux, Benoît
dc.contributor.authorErtaud, Jean-Yves
dc.contributor.editorSkala, Václav
dc.identifier.citationWSCG 2019: Short and Poster papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p.35-43.en
dc.identifier.isbn978-80-86943-38-1 (CD/-ROM)
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464-4625 (CD/DVD)
dc.format9 s.cs
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectdetekce objektůcs
dc.subjectrozpoznávání objektůcs
dc.subjectvizuální sledovánícs
dc.subjectdetekce chodcůcs
dc.subjecthluboké učenícs
dc.subjectvizuální obsluhacs
dc.subjecthistogram orientovaných gradientůcs
dc.subjectmodel deformovatelné součástics
dc.titleReal Time Pedestrian and Object Detection and Tracking-based Deep Learning. Application to Drone Visual Trackingen
dc.typekonferenční příspěvekcs
dc.description.abstract-translatedThis work aims to show the new approaches in embedded vision dedicated to object detection and tracking for drone visual control. Object/Pedestrian detection has been carried out through two methods: 1. Classical image processing approach through improved Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition methods. In this step, we present our improved HOG/DPM approach allowing the detection of a target object in real time. The developed approach allows us not only to detect the object (pedestrian) but also to estimates the distance between the target and the drone. 2. Object/Pedestrian detection-based Deep Learning approach. The target position estimation has been carried out within image analysis. After this, the system sends instruction to the drone engine in order to correct its position and to track target. For this visual servoing, we have applied our improved HOG approach and implemented two kinds of PID controllers. The platform has been validated under different scenarios by comparing measured data to ground truth data given by the drone GPS. Several tests which were ca1rried out at ESIGELEC car park and Rouen city center validate the developed platform.en
dc.subject.translatedobject detectionen
dc.subject.translatedobject recognitionen
dc.subject.translatedvisual trackingen
dc.subject.translatedpedestrian detectionen
dc.subject.translateddeep learningen
dc.subject.translatedvisual servoingen
dc.subject.translatedhistogram of oriented gradientsen
dc.subject.translateddeformable part modelen
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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/35632

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