Open AccessMultiple vehicles detection and tracking for intelligent transport systems using machine learning approaches
Author(s) -
Ngoc Dung Bui,
Manh Dzung Lai,
Tran Vu Hieu,
T H Nguyen Binh
Publication year - 2019
Publication title -
tạp chí khoa học giao thông vận tải/transport and communications science journal
Language(s) - English
Resource type - Journals
eISSN - 2615-9554
pISSN - 1859-2724
DOI - 10.25073/tcsj.70.3.29
Subject(s) - background subtraction , computer science , artificial intelligence , intelligent transportation system , kalman filter , tracking (education) , vehicle tracking system , field (mathematics) , computer vision , optical flow , machine learning , engineering , pixel , image (mathematics) , psychology , pedagogy , civil engineering , mathematics , pure mathematics
Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.