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Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based
Roxana Velazquez-Pupo1, Alberto Sierra-Romero2, Deni Torres-Roman3
1Center for Advanced Research and Education of the National Polytechnic Institute of Mexico, CINVESTAV Guadalajara, Zapopan C.P. 45019, Mexico. rvelazquez@gdl.cinvestav.mx.
Sensors (Basel, Switzerland)
|February 1, 2018
Summary
This study introduces an advanced vision system for real-time traffic surveillance. It accurately detects, tracks, and classifies vehicles, even with occlusions, using Gaussian Mixture Models and Kalman filters.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Traffic Engineering
Background:
- Traffic surveillance systems require robust methods for vehicle detection and classification.
- Occlusion presents a significant challenge in accurately analyzing traffic flow.
Purpose of the Study:
- To develop a high-performance, vision-based traffic surveillance system.
- To enable real-time vehicle detection, tracking, counting, and classification with occlusion handling.
Main Methods:
- Utilized adaptive Gaussian Mixture Model (GMM) for background segmentation.
- Implemented an occlusion reduction algorithm and adaptive Kalman filter for tracking.
- Extracted geometric features (area, height, width) for classification using One Class Support Vector Machine (OC-SVM).
Main Results:
- Achieved real-time performance with an occlusion index of 0.312.
- Obtained a global detection rate (recall) and precision up to 98.190%.
- Reached a high F-measure of 99.051% for midsize vehicle classification.
Conclusions:
- The proposed vision system effectively handles occlusions in traffic surveillance.
- The system demonstrates high accuracy in real-time vehicle detection, tracking, and classification.
- Geometric features combined with OC-SVM provide a reliable method for categorizing vehicle sizes.
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