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: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2016
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| 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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| Summary: | 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. |
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