Welcome to your guide on using CoTracker, a cutting-edge, transformer-based model designed for tracking points in videos. This advanced model combines the benefits of Optical Flow with its unique capabilities to track a variety of pixel points efficiently. Whether you are working with a specific pixel or a quasi-dense set of pixels, CoTracker has got you covered!

What is CoTracker?

CoTracker, as introduced in CoTracker: It is Better to Track Together, allows for remarkable flexibility in video tracking. It can track:

• Any pixel in a video
• A quasi-dense set of pixels together
• Points that can either be manually selected or sampled on a grid from any video frame

How to Use CoTracker

Using CoTracker can be broken down into two main modes: offline and online. Below, we will walk through each step in a user-friendly manner.

Using CoTracker in Offline Mode

To utilize CoTracker in offline mode, follow these steps:

1. First, install the necessary library:

pip install imageio[ffmpeg]

2. Next, download your video:

url = "https://github.com/facebookresearch/co-tracker/blob/main/assets/apple.mp4"

3. Then read the video into frames:

import imageio.v3 as iio
frames = iio.imread(url, plugin='FFMPEG')

4. Now, create a tensor for the video:

import torch
device = 'cuda'
grid_size = 10
video = torch.tensor(frames).permute(0, 3, 1, 2)[None].float().to(device)

5. Run the offline CoTracker:

cotracker = torch.hub.load('facebookresearch/co-tracker', 'cotracker2').to(device)
pred_tracks, pred_visibility = cotracker(video, grid_size=grid_size)

Using CoTracker in Online Mode

For online processing, follow these steps:

1. Initialize the online CoTracker:

cotracker = torch.hub.load('facebookresearch/co-tracker', 'cotracker2_online').to(device)

2. Process your video:

cotracker(video_chunk=video, is_first_step=True, grid_size=grid_size)

3. For each chunk, run the processing in a loop:

for ind in range(0, video.shape[1] - cotracker.step, cotracker.step):
    pred_tracks, pred_visibility = cotracker(video_chunk=video[:, ind : ind + cotracker.step * 2])

This online processing is notably more memory-efficient, allowing you to work with longer videos or process them in real time.

Troubleshooting Tips

If you encounter any challenges while using CoTracker, here are some helpful troubleshooting ideas:

• Ensure that all required libraries are properly installed and up to date.
• Check your video URL for any typos or accessibility issues.
• Make sure your device supports CUDA if you are attempting to utilize GPU acceleration.
• If you experience memory issues, consider reducing the grid size or utilizing online processing.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

CoTracker offers a powerful solution for tracking points in videos, bringing together advanced technology and ease of use. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.