7.1 2D Tracking Fundamentals

Motion tracking is a crucial skill in post-production. 2D tracking fundamentals lay the groundwork for adding effects, stabilizing footage, and more. Understanding tracking points, keyframes, and parameters is essential for achieving accurate and stable results.

Advanced techniques like planar tracking take things further. By mastering these concepts, you'll be able to tackle complex tracking tasks and create seamless visual effects. The skills you learn here will be invaluable throughout your post-production journey.

Tracking Basics

Identifying and Tracking Points

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Top images from around the web for Identifying and Tracking Points

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  Frontiers | Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose With Multiple ... View original

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  Frontiers | Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional ... View original

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  Frontiers | Predicting Motion Patterns Using Optimal Paths View original

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  Frontiers | Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose With Multiple ... View original

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  Frontiers | Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional ... View original

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• Tracking points are specific features or patterns in an image that can be consistently identified and tracked across multiple frames
• Feature detection algorithms analyze the image to find high-contrast, unique patterns that are suitable for tracking (corners, edges, textures)
• Once tracking points are identified, their positions are recorded in each frame to create motion paths
• Motion paths represent the movement of the tracking points over time and can be used to analyze or reproduce the motion

Using Keyframes in Tracking

• Keyframes are specific frames where the tracking data is recorded and stored
• Tracking typically starts on the first frame and ends on the last frame, with keyframes generated automatically at regular intervals
• Users can also manually set keyframes to mark important positions or changes in the motion path
• Keyframes allow for precise control and editing of the tracking data, enabling users to refine the motion path as needed

Tracking Parameters

Configuring the Track Window

• The track window is a region around the tracking point that defines the area to be analyzed in each frame
• Adjusting the size and shape of the track window can improve tracking accuracy by focusing on the most relevant features
• A smaller track window can provide more precise tracking but may lose the point if it moves outside the window
• A larger track window can handle more movement but may include irrelevant features that interfere with tracking

Setting the Search Area

• The search area is the region around the track window where the algorithm looks for the tracking point in the next frame
• Increasing the search area allows the tracker to handle larger movements or faster motion but may slow down the tracking process
• Decreasing the search area can speed up tracking but may lose the point if it moves too far between frames
• The optimal search area size depends on the expected motion and the complexity of the image

Adjusting the Correlation Threshold

• The correlation threshold determines how closely the tracked feature in the current frame must match the original feature from the first frame
• A higher threshold requires a stronger match and can prevent the tracker from drifting to incorrect features, but may lose the point more easily
• A lower threshold allows for more flexibility in matching and can maintain tracking through changes in appearance, but may drift to incorrect features
• The ideal correlation threshold balances tracking stability and adaptability based on the specific footage and desired results

Applying Drift Correction

• Drift correction is a technique used to minimize the accumulation of tracking errors over time
• As the tracker follows the motion path, small inaccuracies can compound, causing the tracked point to drift away from the intended feature
• Drift correction methods, such as periodic re-alignment or keyframe-based correction, can help maintain tracking accuracy
• Re-aligning the tracker to the original feature at regular intervals or using manual keyframes to correct any drift can improve the overall tracking results

Advanced Tracking

Utilizing Planar Tracking Techniques

• Planar tracking is a specialized tracking method designed for tracking flat, textured surfaces (signs, walls, floors)
• Instead of tracking individual points, planar tracking analyzes the entire surface and estimates its motion, rotation, and perspective changes
• Planar tracking can provide more robust and stable tracking for scenes with flat, well-defined surfaces
• Applications of planar tracking include replacing signs, adding virtual elements to walls, or stabilizing footage based on a planar reference
• Planar tracking algorithms often use feature detection and matching techniques to identify and track the surface across frames
• The tracked surface can be used as a reference for compositing, allowing graphics or videos to be realistically integrated into the scene