Detecting Car Speed & Empty Parking Spot with Pytorch & CNN
Detecting Car Speed & Empty Parking Spot with Pytorch & CNN, available at $54.99, has an average rating of 4.69, with 23 lectures, based on 8 reviews, and has 2801 subscribers.
You will learn about 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 This course is ideal for individuals who are People who are interested in building car speed detection system using OpenCV, Pytorch, and SSD or People who are interested in building empty parking spot detection system using OpenCV, Keras, and CNN It is particularly useful for People who are interested in building car speed detection system using OpenCV, Pytorch, and SSD or People who are interested in building empty parking spot detection system using OpenCV, Keras, and CNN.
Enroll now: Detecting Car Speed & Empty Parking Spot with Pytorch & CNN
Summary
Title: Detecting Car Speed & Empty Parking Spot with Pytorch & CNN
Price: $54.99
Average Rating: 4.69
Number of Lectures: 23
Number of Published Lectures: 23
Number of Curriculum Items: 23
Number of Published Curriculum Objects: 23
Original Price: $29.99
Quality Status: approved
Status: Live
What You Will Learn
- 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
Who Should Attend
- People who are interested in building car speed detection system using OpenCV, Pytorch, and SSD
- People who are interested in building empty parking spot detection system using OpenCV, Keras, and CNN
Target Audiences
- People who are interested in building car speed detection system using OpenCV, Pytorch, and SSD
- People who are interested in building empty parking spot detection system using OpenCV, Keras, and CNN
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:
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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
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Learn how to conduct accuracy and performance testing on car speed and empty parking spot detection systems
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to the Course
Lecture 2: Table of Contents
Lecture 3: Whom This Course is Intended for?
Chapter 2: Tools, IDE, and Datasets
Lecture 1: Tools, IDE, and Datasets
Chapter 3: Computer Visions Applications in Traffic Management
Lecture 1: Computer Visions Applications in Traffic Management
Chapter 4: How Car Speed Detection System Works?
Lecture 1: How Car Speed Detection System Works?
Chapter 5: How Empty Parking Spot Detection System Works?
Lecture 1: How Empty Parking Spot Detection System Works?
Chapter 6: Importing OpenCV, Numpy, and Pytorch
Lecture 1: Importing OpenCV, Numpy, and Pytorch
Chapter 7: Playing Video Using OpenCV
Lecture 1: Playing Video Using OpenCV
Chapter 8: Detecting Motion Using OpenCV
Lecture 1: Detecting Motion Using OpenCV
Chapter 9: Creating Function to Calculate Car Speed
Lecture 1: Creating Function to Calculate Car Speed
Chapter 10: Building Car Speed Detection System with OpenCV, Pytorch, and SSD
Lecture 1: Building Car Speed Detection System with OpenCV, Pytorch, and SSD
Chapter 11: Setting Speed Limit & Issuing Speeding Ticket
Lecture 1: Setting Speed Limit & Issuing Speeding Ticket
Chapter 12: Calculating Frame Rate
Lecture 1: Calculating Frame Rate
Chapter 13: Testing Car Speed Detection System
Lecture 1: Testing Car Speed Detection System
Chapter 14: Image Processing Using OpenCV
Lecture 1: Image Processing Using OpenCV
Chapter 15: Finding & Downloading Parking Lot Dataset From Kaggle
Lecture 1: Finding & Downloading Parking Lot Dataset From Kaggle
Chapter 16: Extracting Parking Spot Coordinates
Lecture 1: Extracting Parking Spot Coordinates
Chapter 17: Training Empty Parking Spot Detection Model with Keras & CNN
Lecture 1: Training Empty Parking Spot Detection Model with Keras & CNN
Chapter 18: Building Empty Parking Spot Detection System with OpenCV
Lecture 1: Building Empty Parking Spot Detection System with OpenCV
Chapter 19: Counting How Many Empty Parking Spot
Lecture 1: Counting How Many Empty Parking Spot
Chapter 20: Testing Empty Parking Spot Detection System
Lecture 1: Testing Empty Parking Spot Detection System
Chapter 21: Conclusion & Summary
Lecture 1: Conclusion & Summary
Instructors
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Christ Raharja
Ex Technology Risk Consultant, and E-commerce enthusiast
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- 4 stars: 3 votes
- 5 stars: 5 votes
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