TY  - CONF
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938807090&doi=10.1109%2fICCOINS.2014.6868357&partnerID=40&md5=116855547238c922f342164b7d78e104
A1  - Bhaskar, P.K.
A1  - Yong, S.-P.
SN  - 9781479943913
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2014///
KW  - Gaussian distribution; Highway traffic control; Image processing; Mathematical morphology; Object detection; Tracking (position); Vehicles
KW  -  Detection and tracking; Gaussian Mixture Model; Morphological operations; Rectangular regions; Traffic management; Traffic surveillance; Vehicle counting; Vehicle detection
KW  -  Object tracking
TI  - Image processing based vehicle detection and tracking method
ID  - scholars4214
N1  - cited By 60; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912
N2  - Vehicle detection and tracking plays an effective and significant role in the area of traffic surveillance system where efficient traffic management and safety is the main concern. In this paper, we discuss and address the issue of detecting vehicle / traffic data from video frames. 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 Gaussian mixture model and blob detection methods. First, we differentiate the foreground from background in frames by learning the background. Here, foreground 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. The results are encouraging and we got more than 91 of average accuracy in detection and tracking using the Gaussian Mixture Model and Blob Detection methods. © 2014 IEEE.
AV  - none
ER  -