Vehicle Counting and Detective System

Abstract

One of the most urgent issues of modern urban settings is traffic congestion, road safety and ineffective traffic control. Real-time and accurate information concerning the flow of the vehicles is important in the planning, monitoring and control of transportation systems.
Conventional vehicle counting techniques like manual inspection and sensor-based systems have drawbacks such as expensive installation, not scalable, environmental sensitivity and not able to provide detailed classification of vehicles.
This project is a Vehicle Count and Detection System with Computer Vision and Deep Learning, which has been implemented in main language Python. The system uses video feeds of surveillance cameras and processes them with Open CV and state-of-the-art object detection models like YOLO (You Only Look Once).
The proposed solution has the potential of identifying, monitoring, classifying, and enumerating various categories of vehicles in real time, such as cars, buses, trucks, and two-wheelers. The system can eliminate the issue of double-counting and is accurate even in traffic jams by using tracking algorithms and region-of-interest (ROI) based counting logic.
The system is a cost-effective, non-invasive, and scalable system that can be used in smart city applications and intelligent transportation systems. Traffic statistics generated can help authorities in managing congestion, planning infrastructure and formulation of policies.