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.