Detecting Car Speed & Empty Parking Spot with Pytorch & CNN

Detecting Car Speed & Empty Parking Spots with Pytorch & CNN: A Comprehensive Project-Based Course

Unlock the power of computer vision to revolutionize traffic management! This course provides a hands-on, step-by-step approach to building cutting-edge systems for car speed detection and empty parking spot detection. You'll master OpenCV, Pytorch, and Convolutional Neural Networks (CNNs) to create practical solutions for urban transportation.

What You'll Learn:

• Computer Vision in Traffic Management: Explore real-world applications, use cases, technical limitations, and key technologies.
• Car Speed Detection System:
  • Vehicle Detection: Accurately identify vehicles in video streams.
  • Trajectory Estimation: Track vehicle movement over time.
  • Speed Calculation: Precisely calculate vehicle speed.
  • Speed Limit Check: Implement a system to monitor and enforce speed limits.
  • Speed Ticket Generator: Automate the issuance of speeding tickets.
• Empty Parking Spot Detection System:
  • Data Collection & Preprocessing: Learn how to gather and prepare data for training.
  • Feature Extraction: Identify key characteristics of parking spaces.
  • Object Detection: Detect and locate parking spots.
  • Occupancy Classification: Classify parking spots as occupied or empty.
• OpenCV Fundamentals:
  • Video Playback: Learn to play video streams using OpenCV.
  • Motion Detection: Implement robust motion detection algorithms.
  • Image Processing: Master essential image processing techniques.
  • Custom Function Creation: Develop reusable functions for specific tasks.
• Advanced Techniques:
  • Car Speed Detection with Pytorch & Single Shot Multibox Detector: Build a sophisticated speed detection system.
  • Frame Rate Calculation: Accurately measure video frame rates.
  • Empty Parking Spot Counting: Develop a function to count available parking spaces.
  • Parking Spot Coordinate Extraction: Precisely locate parking spot coordinates.
• Model Training & Evaluation:
  • Keras & CNNs: Train your empty parking spot detection system using Keras and CNNs.
  • Accuracy & Performance Testing: Conduct thorough testing to ensure system reliability.

Project Highlights:

• Kaggle Dataset: Utilize a comprehensive dataset of occupied and unoccupied parking lot images from Kaggle.
• Real-World Impact: Learn how car speed detection can aid law enforcement in road safety and traffic violation enforcement. Discover how empty parking spot detection can reduce parking frustration in urban environments.
• Practical Implementation: Build and test fully functional systems with step-by-step guidance.

This course is ideal for programmers looking to integrate advanced computer vision into traffic management and explore future innovations in urban transportation.