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Vision-Based Vehicle Counting with High Accuracy for Highways with Perspective View

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

Vehicle detection by motion is still a common method used in vision-based tracking systems due to vehicles’ continuous motion on highways. However, counting accuracy is affected for highways with perspective view due to long-time merging (i.e. blob merging or occlusion) events. In this work, a new way of vehicle counting with high accuracy using two appearance-based classifiers is proposed to detect merging situations and handle vehicle counts. Experimental results on three Las Vegas highways with differing perspective views and congestion difficulties show improvement in counting and general applicability of the proposed method. Moreover, tracking and counting results of a highly cluttered highway indicates greater counting improvement (89 % to 94 %) for highly congested situations.

Keywords

Gabor Filter Feature Extraction Technique Vehicle Detection Wavelet Feature Counting Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of NevadaLas VegasUSA

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