Performance Evaluation of Support Vector Machine and Convolutional Neural Network Algorithms in Real-Time Vehicle Type Classification

Abstract. Intelligent traffic management systems needs to obtain information
about traffic with different sensors to control the traffic flow properly. Traffic
surveillance videos are very actively used for this purpose. In this paper, we firstly
create a vehicle dataset from an uncalibrated camera. Then, we test Tiny-YOLO
real-time object detection and classification system and SVM classifier on our
dataset and well-known public BIT-Vehicle dataset in terms of recall, precision,
and intersection over union performance metrics. Experimental results show that
two methods can be used to classify real time streaming traffic video data.

 

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