top of page

Computer Vision



Python, TensorFlow/PyTorch, OpenCV, NumPy aan Matplotlib

Skills to be Learned:

Building and training CNN models for pose estimation

Human Pose Estimation

This immersive, hands-on course is designed to provide students with a comprehensive understanding of computer vision and deep learning for human pose estimation. Human pose estimation is crucial in various fields, including sports analytics, action recognition, and human-computer interaction. Through this course, students will explore the intricacies of pose estimation algorithms and develop the skills to build and implement deep neural networks for this purpose.

This course is a practical and hands-on program designed to teach students the fundamentals of computer vision and deep learning techniques used to estimate and analyze human body poses from images or videos. Human pose estimation plays a critical role in various applications, including action recognition, sports analytics, and human-computer interaction. In this course, students will delve into the complexities of pose estimation algorithms, learn to build and train deep neural networks for this task, and gain practical experience by working on real-world projects. By the end of the course, participants will have the skills needed to develop their own pose estimation systems.

Learning Outcomes:

Upon successful completion of this course, students will:

- Develop a strong foundation in computer vision concepts and techniques.

- Understand the challenges and applications of human pose estimation.

- Gain proficiency in programming with Python and deep learning frameworks.

- Master the process of data preprocessing and augmentation for pose estimation tasks.

- Build and fine-tune deep learning models for accurate pose estimation.

- Be able to evaluate the performance of pose estimation models using relevant metrics.

- Apply human pose estimation techniques to real-world projects and scenarios.


- Proficiency in Python programming.

- Basic understanding of machine learning and deep learning concepts.

- Familiarity with deep learning frameworks like TensorFlow or PyTorch is beneficial but not mandatory.

- Prior experience with computer vision concepts is helpful but not required.

Libraries and Programming Language Used:

- Programming Language: Python

- Deep Learning Framework: TensorFlow or PyTorch

- Computer Vision: OpenCV

- Numerical Computing: NumPy

- Data Visualization: Matplotlib

Course Syllabus:

Introduction to Human Pose Estimation

   - Understanding the importance and applications of human pose estimation.

   - Overview of pose estimation techniques and challenges.

Setting Up the Development Environment

   - Installing Python and essential libraries.

   - Configuring the environment for computer vision and deep learning projects.

Exploring Pose Estimation Datasets

   - Introduction to popular pose estimation datasets.

   - Data loading, preprocessing, and annotation.

Data Preprocessing for Pose Estimation

   - Techniques for preparing image and annotation data for model training.

   - Augmentation strategies to enhance model performance.

Building Pose Estimation Models

   - Creating and training convolutional neural networks (CNNs) for pose estimation.

   - Model architectures and customization for specific pose estimation tasks.

Evaluating Pose Estimation Models

   - Understanding evaluation metrics for pose estimation accuracy.

   - Assessing the effectiveness and limitations of trained models.

Human Pose Estimation Application

   - Applying the knowledge gained throughout the course to develop a practical human pose estimation system.

   - Presenting and demonstrating the project to peers.

The course offers practical experience in tackling real-world projects, ensuring that by its conclusion, participants are fully equipped to create their own pose estimation systems.

bottom of page