Advancements in AI have revolutionized the fields of image generation and large language models. These technologies have become so advanced that it’s hard to tell the difference between AI-generated outputs and real ones.
But it’s not just image generation and language models that have seen rapid progress. Computer vision applications have also made impressive advancements. One such advancement is the segment anything (SAM) model, which can segment objects in images and videos without relying on a training dictionary.
Video analysis has always been challenging due to the complexity of working with motion. Motion tracking is a crucial aspect of video analysis, as it helps in various tasks. This is where the problem lies.
There are two main approaches to motion tracking: optical flow and tracking. Optical flow estimates the velocity of all points within a video frame, while tracking focuses on estimating the motion of individual points over an extended period. However, these methods often treat points as independent, ignoring the correlation between them and leading to inaccurate results.
Enter CoTracker, a neural network-based tracker that aims to revolutionize point tracking in long video sequences. It takes video input and a variable number of starting track locations and outputs the full tracks for the specified points. CoTracker also supports joint tracking of multiple points and can process longer videos by using a windowed application. It uses a transformer-based network with self-attention operators to consider each track as a whole within a window and exchange information between tracks.
One of the advantages of CoTracker is its flexibility in tracking arbitrary points at any spatial location and time in the video. It can refine initial track estimates incrementally to better match the video content. It can also be initialized from any point, even in the middle of a video or from the output of the tracker itself.
CoTracker represents a promising advancement in motion estimation by considering point correlations. It opens up new possibilities for enhanced video analysis and downstream tasks in computer vision.
To learn more about CoTracker, you can check out the Paper, Project, and GitHub. Credit for this research goes to the researchers involved. Also, don’t forget to join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter to stay updated on the latest AI research, cool projects, and more.