Detecting Car Speed & Empty Parking Spot with Pytorch & CNN from Udemy

Welcome to Detecting Car Speed & Empty Parking Spot with Pytorch & CNN course. This is a comprehensive project based course where you will learn step by step on how to build a cutting edge car speed detection system and empty parking spot finder using OpenCV, Convolutional Neural Network, and Pytorch. This course is a perfect combination between computer vision and motion detection, making it an ideal opportunity for you to practice your programming skills while integrating advanced computer vision technologies into traffic management and also open doors for future innovations in urban transportation. In the introduction session, you will learn about computer vision applications in traffic management, such as getting to know its use cases, technologies that will be used, and some technical limitations. Then, in the next session, you learn how the car speed detection system works? This section will cover vehicle detection, trajectory estimation, speed calculation, and speed limit check. In addition, you will also learn how empty parking lot detection systems work. This section will cover the full process from data collection to parking occupancy classification. Before starting the project, we will download a training dataset from Kaggle, the dataset contains hundreds or even thousands of images of occupied parking lots and unoccupied parking lots. We will use this dataset to train the model to be able to distinguish which parking lot has been occupied and which ones have not been occupied by cars. Once everything is ready, we will start the project section, in the first section, you will be guided step by step on how to build a vehicle speed detection system using OpenCV and Pytorch. In addition to that, we will also set a speed limit, so, whenever there is a car exceeding the speed limit, the system will immediately send you a notification and issue a speeding ticket. Meanwhile, in the second project, you will build an empty parking lot detection system using OpenCV and Convolutional Neural Network. Once we have built those detection systems, we will be conducting testing to make sure that they have been fully functioning and all programming logics have been implemented correctly.

First of all, before getting into the course, we need to ask ourselves this question: why should we build a car detection system and empty parking lot detection system? Well, here is my answer, regarding the speed detection system, its implementation can significantly aid law enforcement agencies in enforcing speed limits and enhancing road safety. By accurately detecting and recording vehicle speeds, law enforcement officers can effectively identify and address instances of speeding, thereby reducing the risk of accidents and promoting safer driving behaviors. Moreover, the data collected by the speed detection system can serve as valuable evidence in prosecuting traffic violations, ensuring accountability and deterrence among drivers.On the other hand, the empty parking lot detection system offers numerous benefits to individuals and communities. By providing real-time information on available parking spaces, this system helps to reduce time wasted searching for parking, particularly in densely populated urban areas.

Below are things that you can expect to learn from this course:

Learn about computer vision applications in traffic management, such as getting to know its use cases, technical limitations, and technologies that will be used
Learn how a car speed detection system works. This section will cover vehicle detection, trajectory estimation, speed calculation, speed limit check, and speed ticket generator
Learn how empty parking spot detection systems work. This section will cover data collection, image preprocessing, feature extraction, object detection, and occupancy classification
Learn how to play video using OpenCV
Learn how to detect motion using OpenCV
Learn how to perform image processing using OpenCV
Learn how to create function to detect speed
Learn how to build car speed detection system using OpenCV, Pytorch, and Single Shot Multibox Detector
Learn how to set speed limit and check if the speed exceeds the speed limit
Learn how to create and issue speeding ticket
Learn how to calculate frame rate using OpenCV
Learn how build empty parking spot detection system using OpenCV
Learn how to train empty parking spot detection system using Keras and Convolutional Neural Network
Learn how to create function to count how many empty parking spot
Learn how to extract parking spot coordinate using OpenCV
Learn how to conduct accuracy and performance testing on car speed and empty parking spot detection systems

What's inside

Learning objectives

Learn how to build car speed detection system using opencv, pytorch, and single shot multi box detector
Learn how to train empty parking spot detection system using keras and convolutional neural network
Learn how build empty parking spot detection system using opencv
Learn how to extract parking spot coordinate using opencv
Learn how a car speed detection system works. this section will cover vehicle detection, trajectory estimation, speed calculation, and speed limit check
Learn how empty parking spot detection systems work. this section will cover data collection, image preprocessing, feature extraction, and object detection
Learn how to create function to detect speed
Learn how to set speed limit and check if the speed exceeds the speed limit
Learn how to create and issue speeding ticket

Learn how to calculate frame rate using opencv
Learn how to create function to count how many empty parking spot
Learn about computer vision applications in traffic management, such as getting to know its use cases, technical limitations, and technologies that will be used
Learn how to play video using opencv
Learn how to detect motion using opencv
Learn how to perform image processing using opencv
Learn how to conduct accuracy and performance testing on car speed and empty parking spot detection systems
Show more
Show less

Learn how to build car speed detection system using opencv, pytorch, and single shot multi box detector
Learn how to train empty parking spot detection system using keras and convolutional neural network
Learn how build empty parking spot detection system using opencv
Learn how to extract parking spot coordinate using opencv
Learn how a car speed detection system works. this section will cover vehicle detection, trajectory estimation, speed calculation, and speed limit check
Learn how empty parking spot detection systems work. this section will cover data collection, image preprocessing, feature extraction, and object detection
Learn how to create function to detect speed
Learn how to set speed limit and check if the speed exceeds the speed limit
Learn how to create and issue speeding ticket
Learn how to calculate frame rate using opencv
Learn how to create function to count how many empty parking spot
Learn about computer vision applications in traffic management, such as getting to know its use cases, technical limitations, and technologies that will be used
Learn how to play video using opencv
Learn how to detect motion using opencv
Learn how to perform image processing using opencv
Learn how to conduct accuracy and performance testing on car speed and empty parking spot detection systems
Show more
Show less

Syllabus

Getting to know the general overview of the course and things that you can expect to learn from this course

Introduction to the Course

Table of Contents

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Offers hands-on experience with OpenCV, PyTorch, and CNNs, which are essential tools for developing intelligent transportation systems

Covers both car speed detection and empty parking spot detection, providing a comprehensive understanding of different computer vision applications

Teaches how to train a parking spot detection system using Keras and CNN, which are standard tools and techniques in machine learning

Requires learners to download a dataset from Kaggle, which may require creating an account and agreeing to the platform's terms of service

Explores computer vision applications in traffic management, including use cases, technical limitations, and relevant technologies, which is highly relevant to the field

Teaches how to perform image processing using OpenCV, which is a core skill for computer vision and image analysis tasks

Reviews summary

Practical computer vision projects with pytorch & keras

According to learners, this course offers a highly practical and project-based approach positive to computer vision applications. Students appreciate the hands-on opportunity positive to build functional car speed and parking spot detection systems using libraries like OpenCV, Pytorch, and Keras neutral. While many found the projects rewarding, some reviewers noted facing code and version compatibility issues negative, requiring debugging. It's highlighted as being best for those with a prior foundation in Python and machine learning basics warning, as theoretical concepts may not be covered in deep detail. Overall, it's seen as a strong starting point positive for applying computer vision in real-world scenarios.

Focuses on implementation over deep theory.

"It gives you a starting point, but don't expect a production-ready system without significant work."

"Good overview for getting started in these specific applications."

"The deep learning sections felt a bit rushed."

"Practical, but doesn't dive deep into model architecture or optimization."

Requires background in Python and ML basics.

"Make sure you have a decent Python/ML background coming in, it helps."

"Not for absolute beginners in Python or machine learning."

"I wish they explained the ML concepts more deeply."

"You definitely need some foundational knowledge before tackling this course."

Covers key CV/DL libraries like OpenCV & Pytorch.

"The focus on OpenCV for video/motion and Pytorch/Keras for detection was exactly what I needed."

"Good introduction to applying Pytorch and Keras for these tasks."

"Liked learning how to use OpenCV for these specific computer vision projects."

Strong focus on building practical CV systems.

"Excellent project-based course! Loved building the speed and parking detectors."

"Fantastic practical application of computer vision! Step-by-step guide to building real systems."

"The hands-on coding and projects are the strongest part of the course for me."

"Great for applying theory to real problems instead of just learning concepts."

Some reviewers encountered problems with code.

"Struggled a lot with the code. Many errors due to deprecated libraries."

"Completely outdated code. Could not get anything to run without major fixes."

"Needed to fix bugs and update libraries to make it work correctly."

"Had trouble running the provided code, which seemed outdated in parts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Detecting Car Speed & Empty Parking Spot with Pytorch & CNN with these activities:

Review Convolutional Neural Networks

Show steps

Solidify your understanding of CNNs, which are fundamental to the empty parking spot detection system.

Browse courses on Convolutional Neural Networks

Show steps

Review the basic architecture of CNNs.
Study different layers used in CNNs.
Understand the concept of feature extraction.

Practice OpenCV Fundamentals

Show steps

Sharpen your OpenCV skills, as it's heavily used for both car speed and parking spot detection.

Browse courses on OpenCV

Show steps

Practice reading and displaying images.
Experiment with basic image transformations.
Implement simple object detection using Haar cascades.

Discuss Project Ideas and Challenges

Show steps

Collaborate with peers to brainstorm project ideas and troubleshoot common challenges in computer vision.

Show steps

Form a study group with other students.
Share your initial project ideas and get feedback.
Discuss potential challenges and solutions.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Implement a Basic Motion Detection System

Show steps

Build a simplified motion detection system to reinforce your understanding of OpenCV and image processing techniques.

Show steps

Capture video from a webcam or video file.
Implement background subtraction using OpenCV.
Detect moving objects based on changes in pixel intensity.
Display the detected motion in real-time.

Review: "Computer Vision: Algorithms and Applications"

Show steps

Deepen your understanding of computer vision algorithms with a comprehensive textbook.

View Computer Vision on Amazon

Show steps

Read the chapters on feature extraction and object detection.
Study the algorithms for motion estimation.
Implement some of the algorithms in Python using OpenCV.

Explore PyTorch Object Detection Tutorials

Show steps

Follow online tutorials to gain hands-on experience with PyTorch for object detection tasks.

Show steps

Find tutorials on PyTorch object detection.
Follow the tutorials to implement a basic object detector.
Adapt the code to detect cars in images or videos.

Write a Blog Post on Car Speed Detection Methods

Show steps

Solidify your knowledge by explaining different car speed detection methods in a blog post.

Show steps

Research different car speed detection techniques.
Write a blog post explaining the pros and cons of each method.
Include code examples and visualizations.
Publish your blog post online.

Review: "Programming Computer Vision with Python"

Show steps

Enhance your practical skills in computer vision with a Python-focused guide.

View Scientific Computing with Python - Second... on Amazon

Show steps

Read the chapters on feature extraction and object recognition.
Implement some of the examples in Python using OpenCV.
Experiment with different parameters and settings.

Career center

Learners who complete Detecting Car Speed & Empty Parking Spot with Pytorch & CNN will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

A Computer Vision Engineer develops algorithms and systems that allow computers to 'see' and interpret images, much like the systems explored in this course. This often involves working with technologies like OpenCV, Convolutional Neural Networks, and deep learning frameworks. This course helps build a foundation in these exact technologies, especially with object detection for cars and parking spots. Learning to build systems for both speed detection and parking occupancy can give you a practical project to include in your portfolio. The hands-on experience with PyTorch, OpenCV, and CNNs proves particularly valuable in demonstrating your capabilities to potential employers or clients.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

The role of a Machine Learning Engineer includes designing, building, and deploying machine learning models to solve real-world problems. You will likely need to utilize tools such as PyTorch and Convolutional Neural Networks, exactly as taught in this course. By engaging with the course projects, particularly the empty parking spot detection system, you gain experience in data collection, image preprocessing, feature extraction, and model training for occupancy classification. This practical experience with computer vision and motion detection projects gives you a head start in this field. The knowledge of building and testing systems for both speed and parking availability highlights your capabilities.

See salaries and explore the career path for Machine Learning Engineer

Traffic Analyst

A Traffic Analyst studies traffic patterns and develops strategies to improve traffic flow and safety. This course helps build skills related to computer vision applications in traffic management, as it covers vehicle detection, speed calculation, and data analysis. The knowledge of detecting car speed and identifying empty parking spots is relevant for optimizing traffic flow and reducing congestion. The ability to conduct accuracy and performance testing on detection systems is also valuable for evaluating the effectiveness of traffic management strategies. This is a valuable opportunity.

See salaries and explore the career path for Traffic Analyst

Smart City Consultant

A Smart City Consultant advises cities on how to use technology to improve the quality of life for residents. This course helps build skills related to computer vision applications in traffic management, such as detecting car speeds and empty parking spots. These systems can provide valuable data for optimizing traffic flow, reducing congestion, and improving parking availability. The knowledge gained from this course can be applied to develop innovative solutions for smart cities. The consultant has expertise greatly enhanced.

See salaries and explore the career path for Smart City Consultant

AI Application Developer

An AI Application Developer builds and deploys AI-powered applications for various industries. This course will prove useful as it teaches skills in computer vision, specifically object detection. By learning to build systems for both speed detection and parking spot identification, you demonstrate your ability to apply AI to solve real-world problems. Working with PyTorch and Convolutional Neural Networks enhances your capabilities. It proves your commitment to the field.

See salaries and explore the career path for AI Application Developer

Image Processing Specialist

An Image Processing Specialist works on enhancing, analyzing, and manipulating digital images for various applications. This course helps reinforce the use of OpenCV for image processing tasks, such as detecting motion and extracting features from images. The skills gained in this course are directly applicable to an image processing specialist role. The enhancement to knowledge helps the specialist.

See salaries and explore the career path for Image Processing Specialist

Transportation Planner

A Transportation Planner develops plans and policies for transportation systems, often incorporating technology to improve efficiency and sustainability. This course may be useful as it covers computer vision applications in traffic management, such as detecting car speeds and empty parking spots. These systems can provide real-time data to optimize transportation networks. The insights gained from this course are relevant for creating more efficient and sustainable transportation solutions. The work of a transportation planner is enhanced.

See salaries and explore the career path for Transportation Planner

Data Scientist

A Data Scientist uses data to discover insights and solve problems, which could involve using computer vision techniques for applications like traffic management. This course helps by giving hands-on experience with image processing using OpenCV, training models using Keras and Convolutional Neural Networks, and deploying those models for object detection. The course's focus on building systems for detecting car speed and empty parking spots provides valuable project experience that you can showcase. The skills gained in this course are directly applicable to a data scientist role, particularly in projects related to urban planning, transportation, and smart city initiatives. This course may be useful.

See salaries and explore the career path for Data Scientist

Computer Vision Researcher

A Computer Vision Researcher investigates new algorithms and techniques for enabling computers to 'see' and interpret images. This course may be useful as it exposes you to practical applications of computer vision, such as detecting car speeds and empty parking spots. While research often requires advanced degrees (master's or PhD), the hands-on experience with OpenCV, PyTorch, and CNNs provides a solid foundation in these technologies. This course may play in a part in building research experience.

See salaries and explore the career path for Computer Vision Researcher

Robotics Engineer

A Robotics Engineer designs, builds, and programs robots for various applications. This course may be useful as it focuses on computer vision, which is crucial for enabling robots to perceive and interact with their environment. The course focus on object detection, motion detection, and image processing provides valuable skills for a robotics engineer working on autonomous systems. The practical experience gained from the projects in this course may be valuable. The opportunity to work with OpenCV and PyTorch on real-world problems makes this course worth your while.

See salaries and explore the career path for Robotics Engineer

Autonomous Vehicle Engineer

An Autonomous Vehicle Engineer designs and implements the perception and decision-making systems for self-driving cars. This course may be useful, especially as it directly addresses vehicle detection, speed calculation, and the use of computer vision for real-time decision making. The course emphasis on OpenCV, PyTorch, and CNNs gives you hands-on experience with tools commonly used in developing autonomous vehicle systems. The project involving car speed detection and issuing speeding tickets is particularly relevant. The experience this course provides is valuable when you are hoping to break into the autonomous vehicle industry.

See salaries and explore the career path for Autonomous Vehicle Engineer

Surveillance System Developer

A Surveillance System Developer designs and implements surveillance systems for security and monitoring purposes. This course may be useful as it teaches skills related to vehicle detection, motion detection, and image processing, which are essential for building effective surveillance systems. The ability to detect car speeds, identify empty parking spots, and track vehicle trajectories is relevant for various surveillance applications. This course may add to the knowledge of the system developer.

See salaries and explore the career path for Surveillance System Developer

Video Analyst

A Video Analyst analyzes video footage to identify patterns, trends, and anomalies for various purposes, such as security, traffic monitoring, and market research. This course may be useful as it covers techniques for playing video, detecting motion, and processing images using OpenCV, which are essential skills for a video analyst. The ability to detect car speeds, identify empty parking spots, and track vehicle trajectories is relevant for analyzing traffic patterns and identifying potential safety hazards. The skillset is made more versatile.

See salaries and explore the career path for Video Analyst

Software Engineer

A Software Engineer designs, develops, and tests software applications. This course may be useful by providing exposure to computer vision techniques and tools, such as OpenCV and PyTorch. The experience gained from building car speed detection and empty parking spot detection systems can be valuable for developing software solutions for traffic management or smart city applications. The enhancement to knowledge improves the chances of success.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers are responsible for building and maintaining the infrastructure that allows data to be used effectively by an organization. Although this role is less directly involved with model building, the course helps by providing a practical understanding of the data pipelines used in computer vision applications. You might design systems to efficiently store and retrieve image data. This course may be useful.

See salaries and explore the career path for Data Engineer

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Detecting Car Speed & Empty Parking Spot with Pytorch & CNN.

Computer Vision

Save

Provides a comprehensive overview of computer vision algorithms and techniques. It covers topics such as image processing, feature extraction, object detection, and motion estimation, all of which are relevant to this course. It serves as a valuable reference for understanding the underlying principles behind the car speed and parking spot detection systems. This book is commonly used as a textbook in computer vision courses.

Scientific Computing with Python - Second Edition

Save

Provides a practical introduction to computer vision using Python and OpenCV. It covers a wide range of topics, including image processing, feature extraction, object recognition, and 3D vision. It is particularly useful for learning how to implement computer vision algorithms in Python. This book is more valuable as additional reading than it is as a current reference.

Scientific Computing with Python - Second Edition:...

Paperback

Detecting Car Speed & Empty Parking Spot with Pytorch & CNN

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Practical computer vision projects with pytorch & keras

Activities

Career center

Reading list

Share

Similar courses