CoTracker: It is Better to Track Together
The CoTracker paper proposes a groundbreaking approach that takes video motion prediction to the next level. Traditional methods have often been limited, either tracking the motion of all points in a frame collectively using optical flow, or tracking individual points through a video. These approaches tend to overlook the crucial interrelationships between multiple points, especially when they're part of the same physical object. CoTracker flips the script by employing a transformer-based architecture to jointly track multiple points throughout a video, effectively modeling the correlations between different points in time.
What really sets CoTracker apart is its versatility and adaptability. It's engineered to handle extremely long videos through a unique sliding-window mechanism, and iteratively updates estimates for multiple trajectories. The system even allows for the addition of new tracking points on-the-fly, offering unmatched flexibility. CoTracker outshines state-of-the-art methods in nearly all benchmark tests.
Paper link: https://arxiv.org/abs/2307.07635
Code link: https://github.com/facebookresearch/co-tracker
Project link: https://co-tracker.github.io/
A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-cotracker
#deeplearning #cv #objecttracking
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