Density Based Clustering Methods for Road Traffic Estimation

Abstract

Multiple object detection using deep neural networks can lead to transportation vehicles estimate, a necessary requirement for prediction and management of road traffic and parking lot. Highly overlapped objects that look similar and objects that are there at far distances have lesser probability of detection by state-of-art techniques. We propose techniques to estimate the traffic at regions of poor detection probability in the image based on (i) density based clustering and (ii) exclusive object detection in the regions of poor detection. The proposed techniques lead to better estimation in comparison to state-of-art by approximately 12 %. We have utilized RetinaNet and YOLOv3 networks for object detection.