Title:	Real Time Pedestrian and Object Detection and Tracking-based Deep Learning. Application to Drone Visual Tracking
Authors:	Khemmar, Redouane Gouveia, Matthias Decoux, Benoît Ertaud, Jean-Yves
Citation:	WSCG 2019: Short and Poster papers proceedings: 27. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p.35-43.
Issue Date:	2019
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/35632
ISBN:	978-80-86943-38-1 (CD/-ROM)
ISSN:	2464–4617 (print) 2464-4625 (CD/DVD)
Keywords:	detekce objektů;rozpoznávání objektů;vizuální sledování;sledování;detekce chodců;hluboké učení;vizuální obsluha;histogram orientovaných gradientů;HOG;DPM;model deformovatelné součásti
Keywords in different language:	object detection;object recognition;visual tracking;tracking;pedestrian detection;deep learning;visual servoing;histogram of oriented gradients;HOG;DPM;deformable part model
Abstract in different language:	This 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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG '2019: Short Papers Proceedings

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