[UDM] Object Tracking, Detection, Car Speed, Pose Estim in Python Course (Feb 2025) + Resources

Machine vision With Python: An exploration
Get into the thrilling field of machine vision and artificial intelligence and learn to apply video-reality in Python. In the Object Tracking, Detection, Car Speed, Pose Estim in Python Course, you will learn to create end-to-end solutions to object tracking, velocity estimation, pose estimation, and so on. Each of the projects is executed in the Google Colab environment, so you do not have to install any complex software. This course will take you through a step-by-step and practical through-line, whether you are a novice or you want to advance your command.

Following Mobile Targets and Assessing Their Speed
Here, you will get to know effective algorithms such as ByteTrack and BotSort, with which you will be able to track multiple objects at the same time. You will also obtain knowledge on how to estimate videos of vehicle speeds using YOLOv8. It introduces the theoretical ideas along with practical projects and demonstrates how to gain useful information even from simple video recordings and even in such fields as traffic monitoring and smart cities.

Get to know how to construct personalized models of detection, segmentation, and pose estimation
In this step, you dip your foot in the water of the training machine learning model. You train object detection models as a way to recognize some given objects, use instance segmentation to precisely separate objects on a pixel level, and carry out pose estimation to determine important aspects of bodies or objects. These projects are grounded on real statistics and instruct you on methods of arranging the data, preparing the models, and testing their effectiveness.

Use models in actual projects
By the end of the Object Tracking, Detection, Car Speed, Pose Estim in Python Course, you will know how to test and use the models that you built in live videos or real life. As simple as the ability for tracking footballers to more complex models of car motion analysis, you will be eager to use the deep learning models in practice. Not only will you be familiar with how these systems operate after taking this course, but you will be in a position to implement and operate such systems to carry out either personal or commercial projects.