Title: Vehicle tracking and speed estimation using view-independent traffic cameras

Authors: N. Kavitha; D.N. Chandrappa

Addresses: Research Centre, Department of ECE, SJB Institute of Technology, India; Department of ECE, Dayananda Sagar Academy of Technology and Management, India ' Department of ECE, SJB Institute of Technology, Kengeri Bangalore, India

Abstract: The traditional method's speed estimation accuracy depends on environmental changes and the new CNN technique accurately estimates speed only for quality video but input lacks, which leads to overfitting. Hence proposed method uses the Kalman filter and Hungarian algorithm to track detected vehicles for estimating the speed. First, vehicles are detected using the k-nearest neighbour (kNN) classifier-based background subtraction technique. Then logistic regression value is mapped to a binary value called decision threshold which accurately detects the vehicles. Secondly, each detected vehicle is tracked by a Kalman filter, then the Hungarian algorithm connects all the predictions and produces the tracking results. Finally, the vehicle speed is estimated by measuring the distance between the detected vehicle and the virtual line tracked vehicles on the ground plane. It also stores over-speeding vehicle images with speed. The proposed technique is verified with the BMVC standard database which shows a maximum error of ±2 kmph.

Keywords: foreground image; Kalman filter; Hungarian algorithm; multiple vehicle tracking; speed estimation; motion prediction; morphology operation; vehicle detection; background subtraction; median filter.

DOI: 10.1504/IJAPR.2020.111531

International Journal of Applied Pattern Recognition, 2020 Vol.6 No.2, pp.163 - 176

Received: 19 Jun 2020
Accepted: 03 Sep 2020
Published online: 30 Nov 2020 *

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