Detecting Car Speed & Empty Parking Spot With Pytorch & Cnn

Build car speed detection system and empty parking spot detection system using OpenCV, Pytorch, CNN, Keras, and SSD

What you'll 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

Requirements

No previous experience in object detection is required

Basic knowledge in Python and Pytorch

Description

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 usedLearn how a car speed detection system works. This section will cover vehicle detection, trajectory estimation, speed calculation, speed limit check, and speed ticket generatorLearn how empty parking spot detection systems work. This section will cover data collection, image preprocessing, feature extraction, object detection, and occupancy classificationLearn how to play video using OpenCVLearn how to detect motion using OpenCVLearn how to perform image processing using OpenCVLearn how to create function to detect speedLearn how to build car speed detection system using OpenCV, Pytorch, and Single Shot Multibox DetectorLearn how to set speed limit and check if the speed exceeds the speed limitLearn how to create and issue speeding ticketLearn how to calculate frame rate using OpenCVLearn how build empty parking spot detection system using OpenCVLearn how to train empty parking spot detection system using Keras and Convolutional Neural NetworkLearn how to create function to count how many empty parking spotLearn how to extract parking spot coordinate using OpenCVLearn how to conduct accuracy and performance testing on car speed and empty parking spot detection systems

Overview

Section 1: Introduction

Lecture 1 Introduction to the Course

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Computer Visions Applications in Traffic Management

Lecture 5 Computer Visions Applications in Traffic Management

Section 4: How Car Speed Detection System Works?

Lecture 6 How Car Speed Detection System Works?

Section 5: How Empty Parking Spot Detection System Works?

Lecture 7 How Empty Parking Spot Detection System Works?

Section 6: Importing OpenCV, Numpy, and Pytorch

Lecture 8 Importing OpenCV, Numpy, and Pytorch

Section 7: Playing Video Using OpenCV

Lecture 9 Playing Video Using OpenCV

Section 8: Detecting Motion Using OpenCV

Lecture 10 Detecting Motion Using OpenCV

Section 9: Creating Function to Calculate Car Speed

Lecture 11 Creating Function to Calculate Car Speed

Section 10: Building Car Speed Detection System with OpenCV, Pytorch, and SSD

Lecture 12 Building Car Speed Detection System with OpenCV, Pytorch, and SSD

Section 11: Setting Speed Limit & Issuing Speeding Ticket

Lecture 13 Setting Speed Limit & Issuing Speeding Ticket

Section 12: Calculating Frame Rate

Lecture 14 Calculating Frame Rate

Section 13: Testing Car Speed Detection System

Lecture 15 Testing Car Speed Detection System

Section 14: Image Processing Using OpenCV

Lecture 16 Image Processing Using OpenCV

Section 15: Finding & Downloading Parking Lot Dataset From Kaggle

Lecture 17 Finding & Downloading Parking Lot Dataset From Kaggle

Section 16: Extracting Parking Spot Coordinates

Lecture 18 Extracting Parking Spot Coordinates

Section 17: Training Empty Parking Spot Detection Model with Keras & CNN

Lecture 19 Training Empty Parking Spot Detection Model with Keras & CNN

Section 18: Building Empty Parking Spot Detection System with OpenCV

Lecture 20 Building Empty Parking Spot Detection System with OpenCV

Section 19: Counting How Many Empty Parking Spot

Lecture 21 Counting How Many Empty Parking Spot

Section 20: Testing Empty Parking Spot Detection System

Lecture 22 Testing Empty Parking Spot Detection System

Section 21: Conclusion & Summary

Lecture 23 Conclusion & Summary

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