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Enhanced and effective parallel optical flow method for vehicle detection and tracking

In the area of traffic flow monitoring, planning and controlling, a video based traffic detection and tracking plays an effective and significant role where effective traffic management and safety is the main concern. The goal of the project is to recognize moving vehicles and track them throughout...

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Main Authors: Bhaskar, P.K., Yong, S.-P., Jung, L.T.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995610938&doi=10.1109%2fISMSC.2015.7594042&partnerID=40&md5=b2013bf598070cbc600414b8a912152d
http://eprints.utp.edu.my/30913/
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spelling my.utp.eprints.309132022-03-25T07:43:25Z Enhanced and effective parallel optical flow method for vehicle detection and tracking Bhaskar, P.K. Yong, S.-P. Jung, L.T. In the area of traffic flow monitoring, planning and controlling, a video based traffic detection and tracking plays an effective and significant role where effective traffic management and safety is the main concern. The goal of the project is to recognize moving vehicles and track them throughout their life spans. In this paper, we discuss and address the issue of detecting vehicle/traffic data from video frames with increased real time video processing. Although various researches have been done in this area and many methods have been implemented, still this area has room for improvements. With a view to do improvements, it is proposed to develop an unique algorithm for vehicle data recognition and tracking using Parallel Optical Flow method based on Lucas-Kanade algorithm. Here, Motion detection is determined by temporal differencing and template matching is done only on the locations as guided by the motion detection stage to provide a robust target-tracking method. The foreground optical flow detector detects the object and a binary computation is done to define rectangular regions around every detected object. To detect the moving object correctly and to remove the noise some morphological operations have been applied. Then the final counting is done by tracking the detected objects and their regions in a real time sequence. Results show no false object recognition in some tested frames, perfect tracking for the detected images and 98 tracked rate on the real video with an enhanced real time video processing. © 2015 IEEE. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995610938&doi=10.1109%2fISMSC.2015.7594042&partnerID=40&md5=b2013bf598070cbc600414b8a912152d Bhaskar, P.K. and Yong, S.-P. and Jung, L.T. (2016) Enhanced and effective parallel optical flow method for vehicle detection and tracking. In: UNSPECIFIED. http://eprints.utp.edu.my/30913/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description In the area of traffic flow monitoring, planning and controlling, a video based traffic detection and tracking plays an effective and significant role where effective traffic management and safety is the main concern. The goal of the project is to recognize moving vehicles and track them throughout their life spans. In this paper, we discuss and address the issue of detecting vehicle/traffic data from video frames with increased real time video processing. Although various researches have been done in this area and many methods have been implemented, still this area has room for improvements. With a view to do improvements, it is proposed to develop an unique algorithm for vehicle data recognition and tracking using Parallel Optical Flow method based on Lucas-Kanade algorithm. Here, Motion detection is determined by temporal differencing and template matching is done only on the locations as guided by the motion detection stage to provide a robust target-tracking method. The foreground optical flow detector detects the object and a binary computation is done to define rectangular regions around every detected object. To detect the moving object correctly and to remove the noise some morphological operations have been applied. Then the final counting is done by tracking the detected objects and their regions in a real time sequence. Results show no false object recognition in some tested frames, perfect tracking for the detected images and 98 tracked rate on the real video with an enhanced real time video processing. © 2015 IEEE.
format Conference or Workshop Item
author Bhaskar, P.K.
Yong, S.-P.
Jung, L.T.
spellingShingle Bhaskar, P.K.
Yong, S.-P.
Jung, L.T.
Enhanced and effective parallel optical flow method for vehicle detection and tracking
author_facet Bhaskar, P.K.
Yong, S.-P.
Jung, L.T.
author_sort Bhaskar, P.K.
title Enhanced and effective parallel optical flow method for vehicle detection and tracking
title_short Enhanced and effective parallel optical flow method for vehicle detection and tracking
title_full Enhanced and effective parallel optical flow method for vehicle detection and tracking
title_fullStr Enhanced and effective parallel optical flow method for vehicle detection and tracking
title_full_unstemmed Enhanced and effective parallel optical flow method for vehicle detection and tracking
title_sort enhanced and effective parallel optical flow method for vehicle detection and tracking
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995610938&doi=10.1109%2fISMSC.2015.7594042&partnerID=40&md5=b2013bf598070cbc600414b8a912152d
http://eprints.utp.edu.my/30913/
_version_ 1738657174244032512
score 13.211869

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