What you would learn in Object Tracking using Python and OpenCV course?
The object tracking sub-field is one of Computer Vision designed to find an object in each frame of a video. An excellent example of an application is a surveillance video and security system where suspicious behavior can be identified. Some other examples include the monitoring of traffic on highways and the analysis of the player's movements during a soccer game! In the last instance, you can follow the entire path that the player took during the game.
To help you get to this level to help you get there, in this class, you will master the most important algorithm for tracking objects employing the Python language as well as its OpenCV library! Learn the fundamental knowledge of twelve (twelve) algorithms and apply the algorithms step-by-step! Use the following algorithms will be taught, including Boosting, MIL (Multiple Instance Learning), KCF (Kernel Correlation Filters) and the CSRT (Discriminative Correlation Filter with Channel and Spatial Reliability), MedianFlow, TLD (Tracking Learning Detection), MOSSE (Minimum Output Sum of Squared) Error), turn (Generic Object Tracking using Regression Networks) Meanshift, the CAMShift (Continuously adaptive Meanshift), Optical Flow Sparse and Optical Dense Flow. By the end of the course, you will understand how to apply the tracking algorithms to videos, and you'll be able to design your project.
Learn the fundamentals of all algorithms, and after that, we'll implement them and test them in the PyCharm IDE. It is important to stress that this course aims to make it as practical as you can. So, don't expect excessively from theoretical aspects as you'll discover the essential elements of every algorithm. The goal of demonstrating each algorithm is to be aware that the different algorithms are utilized in various applications. You can select the most effective one by the problem you're looking to address.
Find objects in videos as well as from the webcam by using Python and OpenCV.
Know the basics of tracking algorithms
Apply 12 tracker algorithms
Learn the distinctions between object tracking and tracking.
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