Automatic Traffic Surveillance System Using Cameras - Electronic Engineering Guide
1. Introduction
The Automatic Traffic Surveillance System Using Cameras is an intelligent monitoring solution designed to analyze road traffic conditions in real-time. It utilizes image processing algorithms and embedded electronics to detect violations, monitor congestion, and collect traffic data automatically.
2. Objectives
• Monitor traffic flow using cameras.
• Detect traffic violations such as red-light running or overspeeding.
• Collect and transmit data to a central server or cloud.
• Improve traffic management and safety.
3. Components Required
• Raspberry Pi / Jetson Nano / Arduino with camera support
• USB/CSI Camera Module (e.g., Pi Camera or USB webcam)
• Micro SD Card (with OS for Pi/Jetson)
• Wi-Fi or Ethernet Module (built-in or external)
• Power Adapter or Battery Pack (5V 2A)
• LEDs or Buzzers (optional for alerts)
• Enclosure and Mounting Accessories
• HDMI Monitor and Keyboard (for setup)
4. System Overview
The system captures live video using a camera module connected to a microcontroller or single-board computer. Images are processed locally or sent to a server for analysis. Detection algorithms identify traffic density, violations, and vehicle movement patterns.
5. Camera and Video Processing Unit
• Camera module captures live road footage.
• Raspberry Pi or Jetson Nano processes video in real-time.
• Frame rate and resolution are adjustable based on lighting and bandwidth.
6. Microcontroller/Microprocessor Interface
• Raspberry Pi is preferred for Python and OpenCV
compatibility.
• GPIO pins can be used to trigger alerts or connect external hardware.
• Arduino may be used for basic interfacing or additional sensor support.
7. Image Processing and Detection Algorithms
• OpenCV library used for image processing.
• Background subtraction for vehicle detection.
• Line crossing method for counting vehicles.
• Optional: License plate recognition using OCR (Tesseract).
8. Data Transmission and Storage
• Data can be sent via Wi-Fi to a server using MQTT/HTTP.
• Local storage on SD card or cloud-based storage (Google Firebase, AWS).
• Log format includes timestamp, vehicle count, violation type.
9. Power Supply and Enclosure Design
• Powered via USB adapter or battery pack (5V, 2A min).
• Enclosed in weatherproof housing for outdoor use.
• Proper cable routing and thermal ventilation recommended.
10. Software and Code Structure
Python Code Snippet for Vehicle Detection:
import cv2
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame,
cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5),
0)
_, thresh = cv2.threshold(blur, 50,
255, cv2.THRESH_BINARY)
contours, _ =
cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
if cv2.contourArea(cnt) > 500:
x,y,w,h =
cv2.boundingRect(cnt)
cv2.rectangle(frame, (x,y), (x+w,y+h),
(0,255,0), 2)
cv2.imshow('Traffic', frame)
if cv2.waitKey(1) & 0xFF ==
ord('q'):
break
cap.release()
cv2.destroyAllWindows()
11. Applications
• Smart city traffic monitoring
• Violation detection (speeding, red-light running)
• Vehicle counting and congestion mapping
• Toll booth automation and surveillance
12. Challenges and Enhancements
• Lighting conditions affect accuracy.
• False positives during high traffic density.
• Enhancements: AI/ML-based object detection (YOLO, TensorFlow), night vision
cameras, edge computing.
13. Conclusion
The Automatic Traffic Surveillance System offers a low-cost, effective approach for traffic monitoring and enforcement. With further improvements and AI integration, it can become a powerful tool in smart urban management.