Detecting Car Speed using Object Detection and Depth Estimation: A Deep Learning Framework

TL;DR

This paper proposes a deep learning framework that uses object detection and depth estimation to measure vehicle speed with handheld devices, aiming to improve speed enforcement and reduce road accidents.

Contribution

It introduces a novel deep learning approach for vehicle speed detection using common mobile devices, bypassing the need for specialized speed measuring equipment.

Findings

Effective speed estimation with mobile devices demonstrated

Potential for widespread deployment in traffic monitoring

Improved accuracy over traditional visual estimation methods

Abstract

Road accidents are quite common in almost every part of the world, and, in majority, fatal accidents are attributed to over speeding of vehicles. The tendency to over speeding is usually tried to be controlled using check points at various parts of the road but not all traffic police have the device to check speed with existing speed estimating devices such as LIDAR based, or Radar based guns. The current project tries to address the issue of vehicle speed estimation with handheld devices such as mobile phones or wearable cameras with network connection to estimate the speed using deep learning frameworks.