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Performance analysis of a surveillance system to detect and track vehicles using Haar cascaded classifiers and optical flow method
University of Science and Technology Chittagong.
Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati.
Department of Computer Science and Engineering, University of Science and Technology Chittagong Foy's Lake, Chittagong, Bangladesh.
Department of Computer Science and Engineering, University of Science and Technology Chittagong Foy's Lake, Chittagong, Bangladesh.
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2017 (English)In: 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 258-263Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the real time vehicle detection and tracking system, based on data, collected from a single camera. In this system, vehicles are detected by using Haar Feature-based Cascaded Classifier on static images, extracted from the video file. The advantage of this classifier is that, it uses floating numbers in computations and hence, 20% more accuracy can be achieved in comparison to other classifiers and features of classifiers such as LBP (Local Binary Pattern). Tracking of the vehicles is carried out using Lucas-Kanade and Horn Schunk Optical Flow method because it performs better than other methods such as Morphological and Correlation Transformations. The proposed system consists of vehicle detection and tracking; and it is evaluated by using real data, collected from the route networks of Chittagong City of Bangladesh.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 258-263
Keywords [en]
Haar like Features, Cascade Classifiers, vehicle detection, vehicle tracking
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-70158DOI: 10.1109/ICIEA.2017.8282853Scopus ID: 2-s2.0-85047501241ISBN: 978-1-5090-6162-4 (print)ISBN: 978-1-5090-6161-7 (electronic)ISBN: 978-1-5386-2103-5 OAI: oai:DiVA.org:ltu-70158DiVA, id: diva2:1235259
Conference
12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, Cambodia, 18-20 June 2017
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251Available from: 2018-07-24 Created: 2018-07-24 Last updated: 2018-08-14

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Andersson, Karl

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