Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/54530
Title: Pedestrian activity prediction based on semantic segmentation and hybrid of machines
Authors: Tran, Diem Phuc
Hoang, Van Dung
Pham, Tri Cong
Luong, Chi Mai
Keywords: Autonomous vehicle
Deep learning
Feature extraction
Object detection
Pedestrian recognition
Semantic segmentation
Issue Date: 2018
Series/Report no.: Journal of Computer Science and Cybernetics;Vol. 34 No. 02 .- P.113–125
Abstract: The article presents an advanced driver assistance system (ADAS) based on a situational recognition solution and provides alert levels in the context of actual traffic. The solution is a process in which a single image is segmented to detect pedestrians’ position as well as extract features of pedestrian posture to predict the action. The main purpose of this process is to improve accuracy and provide warning levels, which supports autonomous vehicle navigation to avoid collisions. The process of the situation prediction and issuing of warning levels consists of two phases: (1) Segmenting in order to definite the located pedestrians and other objects in traffic environment, (2) Judging the situation according to the position and posture of pedestrians in traffic. The accuracy rate of the action prediction is 99.59% and the speed is 5 frames per second.
URI: https://dspace.ctu.edu.vn/jspui/handle/123456789/54530
ISSN: 1813-9663
Appears in Collections:Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

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