How do you measure and compare the results of motion tracking and optical flow in video production?

1 What is motion tracking?

Motion tracking is the process of tracking the position, rotation, scale, and perspective of an object or a point in a video. It can be used for various purposes, such as stabilizing shaky footage, adding graphics or text to a moving scene, replacing or removing elements, or creating 3D animations. Motion tracking can be done manually or automatically, depending on the software and the complexity of the scene. Some common motion tracking software are After Effects, Mocha, Blender, and Nuke.

2 What is optical flow?

Optical flow is the process of estimating the motion of pixels between two consecutive frames in a video. It can be used for different purposes, such as creating slow motion, morphing, retiming, or filling gaps in missing frames. Optical flow analyzes the color, brightness, and contrast of the pixels and creates a vector field that describes their direction and speed. Optical flow can be done in real time or offline, depending on the hardware and the quality of the video. Some common optical flow software are Twixtor, Flowblade, Final Cut Pro, and Premiere Pro.

3 Pros and cons of motion tracking

Before using motion tracking in your video production, it is important to understand its advantages and disadvantages. Motion tracking can create realistic and accurate movements that match the original footage, as well as handle complex and dynamic scenes with multiple objects or points. Additionally, it gives you more control and flexibility over the tracked elements and their parameters. On the other hand, motion tracking can be time-consuming and tedious, especially if you have to do it manually or adjust the tracking data. It can also be affected by noise, blur, occlusion, or low contrast in the video, potentially creating errors or glitches if the tracking points are not consistent or reliable.

4 Pros and cons of optical flow

Optical flow can create smooth and seamless transitions and effects that look natural and organic, as well as handle fast and subtle motions that are difficult to capture or track. It can also fill in the gaps or missing frames in the video without losing quality or continuity. However, optical flow is computationally intensive and slow, particularly for high-resolution or long videos. Additionally, it can be affected by lighting changes, motion blur, or complex textures in the video, and may create artifacts or distortions if the pixel motion is not uniform or coherent.

5 How to measure motion tracking results

Measuring the results of motion tracking requires error metrics that quantify the difference between the tracked and actual positions of objects or points. Common error metrics include mean absolute error (MAE), root mean square error (RMSE), and maximum error. To calculate these, you need a reference or ground truth that represents the true positions of the objects or points. This can be obtained manually by marking or annotating positions in each frame, or by using a calibrated camera or motion capture system. After comparing the tracked positions with the reference positions, you can compute the error metrics using a spreadsheet or script. The lower the error metrics, the better the motion tracking results.

6 How to measure optical flow results

To measure the results of optical flow, you can use quality metrics that quantify the similarity between the original and modified frames in the video. This includes peak signal-to-noise ratio (PSNR), which is the ratio between the maximum possible pixel value and the mean squared error between frames. Structural similarity index (SSIM) is a measure of how well frames preserve the luminance, contrast, and structure of the original video. Visual information fidelity (VIF) measures how well frames convey the information content of the original video. To calculate these quality metrics, you need to have both the original and modified videos that use optical flow, which can then be compared and computed using software or a tool such as FFmpeg, VMAF, or MSU Video Quality Measurement Tool. The higher the quality metrics, the better the optical flow results.

7 How to compare motion tracking and optical flow results

To compare the results of motion tracking and optical flow, you need to have a clear goal and a specific criterion for your video production. For example, if your goal is to stabilize a shaky video, you might want to use motion tracking and compare the error metrics to see how well it reduces the camera movement. If your goal is to create a slow motion effect, you might want to use optical flow and compare the quality metrics to see how well it preserves the details and smoothness of the video. You might also want to consider other factors, such as the speed, ease of use, and compatibility of the software and tools that you use for motion tracking and optical flow. Ultimately, the best technique depends on your needs and preferences as a video producer.

8 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?