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
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.

Robot for Gym Exercise Guidance

Zifei Han, Dalei Jiang, Kunle Li, Chang Liu

Featured Project

TEAM MEMBERS

Dalei Jiang (daleij2)

Zifei Han (zifeih2)

Chang Liu (changl12)

Kunle Li (kunleli2)

PROJECT TITLE

Robot for Gym Exercise Guidance

PROBLEM

In modern society, daily fitness is a necessary life choice for healthy people. When it comes to fitness, the standard of movement is very important. However, hiring a coach exclusively for instruction is sometimes not a convenient and economical option. We think robots are perfectly capable of determining whether a person's movements are in place. To this end, we need to propose a scheme to design a robot that can walk behind people and use certain technologies to identify human movements when people are moving, compare with the existing action models, and give an evaluation.

SOLUTION OVERVIEW

Our solution is to design a robot that included a chassis that drove the motion on the bottom and a computer operating system and camera on the top. With ultrasonic radar and cameras, the robot can follow the target. When the "motion assessment" module starts to operate, the camera will capture video information and begin motion analysis at the same time. The analysis of human motion will be completed as soon as possible and the standard evaluation of motion will be given. At the same time, we will design some multimedia files, such as sound and video, to interact with the user.

SOLUTION COMPONENTS

Based on the introduction above, several systems need to be implemented to realize the solution.

SUBSYSTEM 1: BOTTOM MOBILE PLATFORM PROGRAMMING

We plan to take use of the EAI SMART robot platform as the base movement platform of the robot. We will do the programming based on the ROS system to realize automatic navigation, path planning, and object tracking.

SUBSYSTEM 2: SKELETAL BINDING AND MOVEMENT ANALYSIS OF THE HUMAN BODY

The most important part of this program is that we will use the Mask R-CNN to do the skeletal binding to determine the human's movement. We will try to train an efficient model to help us realize fast analysis.

SUBSYSTEM 3: MAN-MACHINE INTERACTIVE SYSTEM

As a user-oriented product, we need to design a friendly human-computer interface to realize the free conversion of functions.

SUBSYSTEM 4: MOVEMENT STANDARD ALGORITHM

We need to devise an algorithm to assess the deviation between the gymnast's movements and the standard. This algorithm is very important for the final product performance feedback.

CRITERION FOR SUCCESS

The robot can self-navigate to find people in the gym.

The robot can monitor the person doing exercise and extract human poses.

The robot can check whether the person is doing correctly in the exercise.

DISTRIBUTION OF WORK

Dalei Jiang: Skeletal binding and movement analysis of the human body

Zifei Han: Bottom mobile platform programming

Chang Liu: Man-machine interactive system building

Kunle Li: Movement standard algorithm designing