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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.
Vise andre og tillknytning
2017 (engelsk)Inngår i: 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 258-263, artikkel-id 17595122Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017. s. 258-263, artikkel-id 17595122
Emneord [en]
Haar like Features, Cascade Classifiers, vehicle detection, vehicle tracking
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-70158DOI: 10.1109/ICIEA.2017.8282853Scopus ID: 2-s2.0-85047501241ISBN: 978-1-5090-6162-4 (tryckt)ISBN: 978-1-5090-6161-7 (digital)ISBN: 978-1-5386-2103-5 (tryckt)OAI: oai:DiVA.org:ltu-70158DiVA, id: diva2:1235259
Konferanse
12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, Cambodia, 18-20 June 2017
Prosjekter
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Forskningsfinansiär
Swedish Research Council, 2014-4251Tilgjengelig fra: 2018-07-24 Laget: 2018-07-24 Sist oppdatert: 2019-01-18bibliografisk kontrollert

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