Detecting Car Speed & Empty Parking Spot with Pytorch & 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: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.