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Sensors 2018, 18(2), 374; doi:10.3390/s18020374

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

1
Center for Advanced Research and Education of the National Polytechnic Institute of Mexico, CINVESTAV Guadalajara, Zapopan C.P. 45019, Mexico
2
Intel Labs, Intel Tecnología de Mexico, Zapopan C.P. 45019, Mexico
Died at August 2016.
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 7 January 2018 / Accepted: 18 January 2018 / Published: 27 January 2018
(This article belongs to the Special Issue Sensors for Transportation)
View Full-Text   |   Download PDF [4420 KB, uploaded 29 January 2018]   |  

Abstract

This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles. View Full-Text
Keywords: IoT vision system; vehicle classification; One Class Support Vector Machine; vehicle detection; vehicle occlusion index; adaptive Gaussian mixture model; adaptive Kalman filter IoT vision system; vehicle classification; One Class Support Vector Machine; vehicle detection; vehicle occlusion index; adaptive Gaussian mixture model; adaptive Kalman filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Velazquez-Pupo, R.; Sierra-Romero, A.; Torres-Roman, D.; Shkvarko, Y.V.; Santiago-Paz, J.; Gómez-Gutiérrez, D.; Robles-Valdez, D.; Hermosillo-Reynoso, F.; Romero-Delgado, M. Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System. Sensors 2018, 18, 374.

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