Project

# Title Team Members TA Documents Sponsor
18 Real-Time Traffic Monitoring and Congestion Analysis Using Raspberry Pi and Computer Vision
 Ding Jiang
Yiyang Cheng
Yucong Gao
Zetong Lang
 design_document1.pdf
final_paper1.pdf
final_paper3.pdf
 Chao Qian
# Problem
Monitoring urban traffic congestion is a growing challenge for city infrastructure management. Current monitoring methods often rely on manual observation or expensive dedicated hardware, and lacking time sensitivity, making real-time traffic density analysis difficult and costly. Traffic managers lack accessible, affordable tools to continuously monitor vehicle counts and congestion levels, limiting their ability to optimize traffic flow efficiently.
# Solution overview
Our solution is a Raspberry Pi-based traffic monitoring system that uses a camera to capture and analyze traffic density in real-time. This system applies image processing to count vehicles and monitor congestion levels, providing data for optimizing traffic flow. We will also include a live dashboard to visualize traffic density data and to generate congestion alerts automatically, providing an affordable and scalable tool for traffic flow optimization.
# Solution component
## Software component
- Real-time vehicle detection and counting module implemented on Raspberry Pi using computer vision techniques (e.g., OpenCV or lightweight deep learning models). This module captures video streams from the camera and processes frames to identify and count vehicles in traffic.
- Traffic data processing and visualization module that collects vehicle count data, estimates traffic density, and sends the processed data to a real-time dashboard. The dashboard will visualize traffic conditions and generate congestion alerts when vehicle density exceeds predefined thresholds.
## Hardware component
- Raspberry Pi computing unit integrated with a high‑resolution camera module to capture continuous traffic video streams and perform edge computing for image processing.
- Weather-resistant outdoor enclosure with an adjustable camera mounting structure. The enclosure protects the electronics from rain, dust, and temperature variations while maintaining stable camera positioning for reliable long-term monitoring.
# Criteria of success
- The dashboard should collect proper amount of data to determine the extent of congestion and to send alarms at certain level of congestion.
- The whole structure should work continuously under bad weather condition such as rain, snow, etc.
- The dashboard should display relevant traffic data with as minimal latency as possible and report timely.
# Distribution of work
## Ding Jiang:
Responsible for developing the core image processing algorithms on the Raspberry Pi. This includes implementing vehicle detection and counting using computer vision techniques, optimizing the processing pipeline for real-time performance, and integrating the camera input with the processing module. This member also assists in testing the accuracy of vehicle detection under different traffic conditions.
## Yucong Gao:
Responsible for building the real-time traffic monitoring dashboard. Tasks include designing the interface for visualizing vehicle counts and congestion levels, implementing the data pipeline from the Raspberry Pi to the dashboard, and developing congestion alert functions. This member also ensures that the system provides low-latency updates and reliable visualization of traffic density data.
## Yiyang Cheng:
Responsible for the hardware setup of the Raspberry Pi traffic monitoring system. This includes installing and configuring the Raspberry Pi, camera module, and power supply system. This member also handles hardware integration and ensures that the camera and computing unit operate reliably for continuous video capture.
## Zetong Lang:
Responsible for designing the outdoor enclosure and camera mounting structure. The enclosure must protect the system from environmental conditions such as rain, dust, and temperature changes while allowing stable camera positioning. This member will design and prototype the enclosure, ensuring durability and ease of installation.

Drum Tutor Lite

Featured Project

Team: Yuanheng Yan, Zhen Qin, Xun Yu

Vision: Rhythm games such as guitar hero are much easier than playing the actual drums. We want to make a drum tutor that makes playing drums as easy as guitar hero. The player is not required to read a sheet music.

Description: We will build a drum add-on that will tutor people how to play the drums. We will make a panel for visual queue of the drum and beats in a form similar to guitar hero game. The panel can be a N*10 (N varying with the drum kit) led bar array. Each horizontal bar will be a beat and each horizontal line above the bottom line will represent the upcoming beats.

There will be sensors on each drum that will fire when the drum heads is hit. The drums will be affixed with ring of light that provides the timing and accuracy of the player according to the sensors.

Of course with a flip of a switch, the drum could be a simple light up drum: when the player hits the drum, that particular drum will light up giving cool effects.

The system will be on a microprocessor. Or for more versatile uses, it could be connected to the computer. And a app will be written for the tutor.