Performance Comparison of Object Detection Models for Traffic Density Estimation Using CCTV Footages

Abstract

This study evaluated three object detection models for estimating traffic density on Elias Angeles St., Naga City, using mean average precision (mAP). The object detection model classified vehicles into five classes: private cars, jeepneys, trucks, motorcycles, and tricycles. Closed-circuit television (CCTV) footage was subjected to adaptive background subtraction and morphological opening to produce 320px × 320px images for use as a dataset for object detection models. Using Common Objects in Context means Average Precision at Intersection over Union (COCO mAP at IoU=50 as the metric for mAP. Faster Region-Convolutional Neural Network (Faster R-CNN) achieved the highest mAP of 92.54%, compared with You Only Look Once version 3 (YOLOv3) and Single Shot MultiBox Detector (SSD). In the traffic density estimation, vehicle size was accounted for; consequently, a private car was used as the standard vehicle type.