OpenCV provides several object tracking algorithms, including the BOOSTING, MIL, Kernelized Correlation Filter, Discriminative Correlation Filter, MedianFlow, TLD, MOSSE, and GOTURN trackers.

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OpenCV is the largest open-source computer vision library that gives access to thousands of state of the art algorithms with detailed documentation. It has classes/functions for face detection, object identification, extraction of 3D models, tracking of movement, stitching frames, etc. Can be used in C++, Python, Java, and MATLAB. 

OpenCV

The video capture class allows you to process video files into individual frames. You can then use different operators/functions such as read, get, and set to retrieve or manipulate the frames.

Video Capture

Cameras introduce radial and tangential distortion and an import pre-processing step in computer vision. To remove the distortion we need to find the intrinsic and extrinsic parameters of the camera. OpenCv provides functions such as CalibrateCamera, getOptimalNewCameraMatrix, undistort, etc.

Camera Calibration

An affine transformation represents a relationship between two images through rotation, translation, and scaling. OpenCV has a few functions such as affineTransform2D. affineTransform3D which create 2x3 matrix transformation matrices between two coordinates systems in the same dimension.

Affine Transform

Object Tracking in OpenCV

An important pre-processing step in computer vision is converting to different color schemes such as gray or HSV. OpenCV makes the seamless using cv2.cvtColor where you can input you image and use flags such as COLOR_BGR2RGB to convert it between color schemes.

Converting image in OpenCV

Smoothing and blurring is image processing step that reduces noise and detail in an image. It is important for edge detection and thresholding. OpenCV has few different options such as:
- Average blur
- Guassian blur
- Median filtering
- Bilateral filtering

Smoothing & Blurring in OpenCV

In computer vision, feature matching involves finding identical features across different images. OpenCV provides a brute-force matcher technique for this task, which takes a feature descriptor from one image and matches it with others using a distance calculation, which defaults to the L2 norm.

Feature Matching in OpenCV

OpenCV implements the Lucas-Kanade sparse optical flow method via the `cv2.calcOpticalFlowPyrLK()` function. This method performs a least-squares fit on the motion of a 3x3 pixel neighborhood (9 points) around each target point, assuming small movement between consecutive frames to satisfy the brightness constancy constraint. The function accepts the previous frame, the next frame, and the tracking points from the previous frame, returning the updated points and a status vector indicating successful tracking.

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