Confidence based active learning for vehicle classification in urban traffic

Chen, Zezhi, Ellis, Tim and Velastin, Sergio (2012) Confidence based active learning for vehicle classification in urban traffic. In: IV Chilean Workshop on Pattern Recognition; 14 - 15 Nov 2012, Valparaíso, Chile.

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Abstract

This paper presents a framework for confidence based active learning for vehicle classification in an urban traffic environment. Vehicles are automatically detected using an improved background subtraction algorithm using a Gaussian mixture model. A vehicle observation vector is constructed from measurement-based features and an intensity-based pyramid HOG. The output scores of a linear SVM classifier are accurately calibrated to probabilities using an interpolated dynamic bin width histogram. The confidence value of each sample is measured by its probabilities. Thus, only a small number of low confidence samples need to be identified and annotated according to their confidence. Compared to passive learning, the number of annotated samples needed for the training dataset can be reduced significantly, yielding a high accuracy classifier with low computational complexity and high efficiency. The detected vehicles are classified into four main categories: car, van, bus and motorcycle. Experimental results demonstrate the effectiveness and efficiency of our approach. The method is general enough so that it can be used in other classification problems and domains, e.g. pedestrian detection.

Item Type: Conference or Workshop Item (Paper)
Event Title: IV Chilean Workshop on Pattern Recognition
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Faculty of Science, Engineering and Computing > School of Computer Science and Mathematics
Faculty of Science, Engineering and Computing (until 2017)
Faculty of Science, Engineering and Computing (until 2017) > School of Computing and Information Systems
Depositing User: Sergio Velastin
Date Deposited: 29 Apr 2020 09:57
Last Modified: 29 Apr 2020 09:57
URI: http://eprints.kingston.ac.uk/id/eprint/26054

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