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access icon free License number plate recognition system using entropy-based features selection approach with SVM

© The Institution of Engineering and Technology
Received 10/04/2017, Accepted 13/06/2017, Revised 25/05/2017, Published 18/08/2017

License plate recognition (LPR) system plays a vital role in security applications which include road traffic monitoring, street activity monitoring, identification of potential threats, and so on. Numerous methods were adopted for LPR but still, there is enough space for a single standard approach which can be able to deal with all sorts of problems such as light variations, occlusion, and multi-views. The proposed approach is an effort to deal under such conditions by incorporating multiple features extraction and fusion. The proposed architecture is comprised of four primary steps: (i) selection of luminance channel from CIE-Lab colour space, (ii) binary segmentation of selected channel followed by image refinement, (iii) a fusion of Histogram of oriented gradients (HOG) and geometric features followed by a selection of appropriate features using a novel entropy-based method, and (iv) features classification with support vector machine (SVM). To authenticate the results of proposed approach, different performance measures are considered. The selected measures are False positive rate (FPR), False negative rate (FNR), and accuracy which is achieved maximum up to 99.5%. Simulation results reveal that the proposed method performs exceptionally better compared with existing works.

Inspec keywords: image segmentation; support vector machines; feature selection; image colour analysis; image classification; traffic engineering computing; image fusion; feature extraction; entropy

Other keywords: multiple feature extraction; street activity monitoring; geometric features; feature classification; binary segmentation; license number plate recognition system; road traffic monitoring; potential threat identification; LPR system; HOG fusion; security applications; entropy-based features selection approach; image refinement; multiple feature fusion; luminance channel selection; support vector machine; CIE-Lab colour space; SVM; performance measures

Subjects: Sensor fusion; Traffic engineering computing; Knowledge engineering techniques; Computer vision and image processing techniques; Image recognition

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