Build car speed detection system and empty parking spot detection system using OpenCV, Pytorch, CNN, Keras, and SSD
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
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- Course Overview
- Embark on a practical journey into the cutting-edge fusion of computer vision and deep learning, specifically tailored for smart city infrastructure and intelligent transportation systems. This course transcends theoretical concepts, guiding you through the creation of two distinct, high-impact AI applications: a dynamic system for monitoring vehicle speeds and an intelligent solution for identifying vacant parking spaces. You’ll explore the entire lifecycle of a computer vision project, from initial data understanding to model deployment, utilizing industry-standard libraries and frameworks to solve real-world urban challenges.
- Requirements / Prerequisites
- A foundational understanding of Python programming, including familiarity with basic data structures and control flow.
- Conceptual knowledge of machine learning principles, such as supervised learning, training, and testing data splits.
- Basic algebra and calculus can be helpful for grasping the underlying mechanics of neural networks, though not strictly mandatory.
- Comfortable with setting up a development environment and installing Python packages.
- No prior expertise in deep learning or advanced computer vision is required, as essential concepts will be introduced.
- Skills Covered / Tools Used
- Advanced Image Processing: Mastering techniques for image manipulation, feature extraction, and region of interest (ROI) definition using the versatile OpenCV library.
- Deep Learning Framework Proficiency: Gaining hands-on experience with both PyTorch and Keras/TensorFlow for building, training, and evaluating complex neural network architectures.
- Object Detection Architectures: Implementing and fine-tuning state-of-the-art Single Shot MultiBox Detector (SSD) models for robust and efficient object localization.
- Convolutional Neural Networks (CNNs): Designing and understanding the power of CNNs for intricate image classification and pattern recognition tasks.
- Model Training and Evaluation: Developing strategies for effective model training, hyperparameter tuning, and performance assessment using metrics relevant to computer vision.
- Real-time Video Stream Analysis: Techniques for processing sequential image data from video feeds to track objects and infer dynamic properties.
- Spatial Coordinate Mapping: Utilizing computer vision to accurately identify and map real-world spatial information, crucial for applications like parking management.
- Data Annotation & Preparation: Understanding the importance of preparing and structuring datasets for deep learning tasks.
- Benefits / Outcomes
- You will possess a tangible portfolio of two complete, functional computer vision projects applicable to smart city initiatives and beyond.
- You will develop a robust problem-solving methodology for approaching complex image and video analysis challenges.
- You will gain the confidence to implement, train, and deploy deep learning models using leading industry frameworks (PyTorch, Keras).
- You will understand the intricate interplay between classical computer vision techniques and modern deep learning paradigms.
- You will be equipped with skills highly sought after in roles involving AI for autonomous systems, surveillance, traffic management, and urban planning.
- You will be able to interpret and explain the operational mechanics behind sophisticated vision-based systems.
- PROS
- Highly Practical: Focuses on building two distinct, real-world applications from scratch, providing invaluable hands-on experience.
- Diverse Toolset: Covers multiple popular libraries and frameworks (OpenCV, PyTorch, Keras, SSD), broadening your technical versatility.
- Relevant to Industry Trends: Addresses critical applications in smart cities, IoT, and autonomous technology, enhancing employability.
- Comprehensive Coverage: Teaches the end-to-end process of developing computer vision solutions, from data to deployment.
- CONS
- The breadth of topics covered might present a steep learning curve for individuals entirely new to programming or deep learning concepts.
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