Detekce chodců v obrazech pro potřeby autonomních vozidel
Pedestrians Detection in Images for Autonomous Vehicles
Typ dokumentu
diplomová prácemaster thesis
Autor
Di Yang
Vedoucí práce
Oswald Cyril
Oponent práce
Cejnek Matouš
Studijní obor
Přístrojová a řídicí technikaStudijní program
Strojní inženýrstvíInstituce přidělující hodnost
ústav přístrojové a řídící technikyPráva
A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.htmlVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html
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With the rapid development of the world's economy, the number of vehicles is constantly increasing. Due to the driver's subjective error, traffic accidents occur frequently, which seriously threatens the life and safety of pedestrians on the road. The emergence of autonomous vehicles will reduce traffic accidents caused by human factors. Pedestrian detection is the most important technology in the automatic driving systems because man's life is more precious than anything. Pedestrian detection method is a valuable and challenging topic in the field of computer vision because pedestrian with both rigid property and flexible property, whose appearance is easily affected by such factors as clothes, occlusion, scale, posture, and viewed angle. This master thesis processes a pedestrian detection method based on traditional method (HOG+SVM) and neural network method (faster-rcnn). After comparing the detection precision of these two methods, it is obviously to find the second one is better. This thesis mainly carries out the following research 1.Introduce the theory of traditional objective detection method (HOG+SVM) and deep learning method (rcnn, fast-rcnn, faster-rcnn). 2.Due to the limitation of space, the faster- RCNN is selected as the detector algorithm. 3.Training these two detection models and comparing their precious. Verifying the superiority of neural network detector With the rapid development of the world's economy, the number of vehicles is constantly increasing. Due to the driver's subjective error, traffic accidents occur frequently, which seriously threatens the life and safety of pedestrians on the road. The emergence of autonomous vehicles will reduce traffic accidents caused by human factors. Pedestrian detection is the most important technology in the automatic driving systems because man's life is more precious than anything. Pedestrian detection method is a valuable and challenging topic in the field of computer vision because pedestrian with both rigid property and flexible property, whose appearance is easily affected by such factors as clothes, occlusion, scale, posture, and viewed angle. This master thesis processes a pedestrian detection method based on traditional method (HOG+SVM) and neural network method (faster-rcnn). After comparing the detection precision of these two methods, it is obviously to find the second one is better. This thesis mainly carries out the following research 1.Introduce the theory of traditional objective detection method (HOG+SVM) and deep learning method (rcnn, fast-rcnn, faster-rcnn). 2.Due to the limitation of space, the faster- RCNN is selected as the detector algorithm. 3.Training these two detection models and comparing their precious. Verifying the superiority of neural network detector
Kolekce
- Diplomové práce - 12110 [154]