This vehicle identification project utilizes the YOLOv5 deep learning model for detecting and classifying vehicles from images, videos, and live streams. It supports real-time inference, saving outputs with bounding boxes, confidence scores, and class labels, making it ideal for traffic monitoring and smart surveillance systems.

R API client for Bay Area traffic data

Google Maps Traffic Notifier

TCP Traffic Analyzer

IoT based project to monitor traffic using Raspberry Pi, Pi Camera, RFID MFRC522 Module and GPIO pins.

YouTube Channel Traffic Source Tracker monitor

Al-Go is a thesis project by ECE students Balla, Saavedra, and Tejano focused on intelligent traffic monitoring. Pedestrian.py detects and tracks pedestrians and PWDs using a trained model and tracking. Vehicle.py counts vehicles in zones. Program.py manages the Raspberry Pi camera, processes frames, and handles data transmission.

2008: LightCount consists of a daemon (in C) that keeps track of all IP traffic on specified ethernet interfaces and an interface (in python) that reads the data from the MySQL database. Used to monitor bandwidth usage per IP address.

Traffic Vision: AI-Powered Traffic Monitoring System and Signal Optimization

The Flight Control service coordinates a drone fleet by managing flight permissions and creating flight routes. This is a part of the Autonomous Drone Delivery System, which I implemented for my Bachelor's Thesis.

A YOLOv8 based project for real-time traffic density estimation. It employs fine-tuned vehicle detection models to analyze and count vehicles per frame, aiding urban traffic management and planning. The repository includes model training, traffic intensity analysis, and deployment strategies.

Traffic monitoring application that is capable of doing real time car counting under strenuous conditions.

Web-based packet analysis tool for viewing and inspecting PCAP/PCAPNG files. Fast, clean UI, IP filtering, charts, and detailed packet insights.

SSH traffic HTTP spoofer

This repository features a vehicle counting system implemented with OpenCV and Python. It processes video input to detect and count vehicles in real-time, offering accurate traffic data analysis and visualization for efficient traffic monitoring.