Object Tracking using Python and OpenCV

Object tracking is a subarea of Computer Vision which aims to locate an object in successive frames of a video. An example of application is a video surveillance and security system, in which suspicious actions can be detected. Other examples are the monitoring of traffic on highways and also the analysis of the movement of players in a soccer match! In this last example, it is possible to trace the complete route that the player followed during the match.

To take you to this area, in this course you will learn the main object tracking algorithms using the Python language and the OpenCV library! You will learn the basic intuition about 12 (twelve) algorithms and implement them step by step! At the end of the course you will know how to apply tracking algorithms applied to videos, so you will able to develop your own projects. The following algorithms will be covered: Boosting, MIL (Multiple Instance Learning), KCF (Kernel Correlation Filters), CSRT (Discriminative Correlation Filter with Channel and Spatial Reliability), MedianFlow, TLD (Tracking Learning Detection), MOSSE (Minimum Output Sum of Squared) Error), Goturn (Generic Object Tracking Using Regression Networks), Meanshift, CAMShift (Continuously Adaptive Meanshift), Optical Flow Sparse, and Optical Flow Dense.

You'll learn the basic intuition about all algorithms and then, we'll implement and test them using PyCharm IDE. It's important to emphasize that the goal of the course is to be as practical as possible, so, don't expect too much from the theory since you are going to learn only the basic aspects of each algorithm. The purpose of showing all these algorithms is for you to have a view that different algorithms can be used according to the types of applications, so you can choose the best ones according to the problem you are trying to solve.