Open AccessV-DaT: A Robust method for Vehicle Detection and Tracking
Author(s) -
Latha Anuj,
M. T. Gopalakrishna,
C Naveena,
Y. H. Sharath Kumar
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.2092
Subject(s) - background subtraction , computer science , artificial intelligence , thresholding , computer vision , tracking (education) , vehicle tracking system , object detection , bounding overwatch , pattern recognition (psychology) , image (mathematics) , pixel , kalman filter , psychology , pedagogy
Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging.