# CoTracker3: Enhanced Point Tracking with Less Data Oct 23, 2024 ## A new semi-supervised approach achieves state-of-the-art performance with a thousandfold reduction in real data requirements. ## Introduction to Point Tracking In computer vision, point tracking estimates **the movement of specific points within a video over time**. In this task, a model is trained to identify and maintain correspondences between tracked points across multiple frames in a video. However, accurately tracking points in videos isn’t a trivial task. **First, you have to worry about occlusions.** This is a massive pain. I'd put this as the primary challenge for point trackers because objects in a scene can obstruct the view of tracked points, leading to inaccurate tracking. **Second, a tracked point can leave the field of view.** As objects and the camera move, tracked points will go with them, often out of the camera's field of view. This makes it difficult for some trackers to predict their trajectory. **Third, you need to consider the appearance of points.** Lighting and perspective changes can affect how a point looks. Rapid movements can make tracking difficult. Points may appear larger or smaller as objects move closer or farther from the camera. **Last but not least is computational complexity.** Computational complexity becomes a bottleneck when tracking many points simultaneously, especially considering dependencies between tracks. ## The Applications of Point Tracking That Make it a Problem Worth Tackling Despite what some folks may think, computer vision is not a solved problem, and the point tracking task has various applications that push the progress in other areas of computer vision. For example: - Motion Analysis - 3D Reconstruction - Video Editing and Special Effects - Robotics and Autonomous Navigation ## A Brief History of Point Tracking Historically, there have been a few dominant paradigms for this task. ## Optical Flow **Optical flow** models estimate dense instantaneous motion. They can be classical approaches, deep learning methods, or transformer-based methods. - Classical Approaches - Deep Learning-Based Methods - RAFT and its Variants - Transformer-Based Methods Optical flow methods are powerful for estimating dense instantaneous motion but are not ideal for long-term point tracking due to error accumulation. ## Multi-Frame Optical Flow **Multi-frame optical flow** models extend optical flow to multiple frames but are still not designed for long-term tracking or occlusion handling. While initial attempts to extend optical flow to multiple frames relied on [Kalman filtering](https://github.com/NickNair/Multiple-Object-Tracking-using-Kalman-Filter) for temporal consistency, more recent approaches include: - Modern Dense Flow Methods - [Multi-Flow Dense Tracker (MFT):](https://arxiv.org/abs/2305.12998) The methods can estimate flow across multiple frames but are not designed for long-term point tracking and struggle with handling occlusions, especially when points are occluded for a long time. ## Point Tracking **Point tracking** models track sparse sets of points over time. Unlike optical flow, which estimates dense instantaneous motion, point tracking focuses on a **sparse set of points** and tries to maintain their correspondence across multiple frames. Some models that follow this paradigm include: - Particle Video - PIPs - TAP-Vid - TAPIR - PIPs++ - OmniMotion Many point tracking models, like PIPs and PIPs++, track points independently; however, **points in a video often exhibit strong dependencies**, such as belonging to the same object. [The original CoTracker model](https://co-tracker.github.io/) uses these dependencies by performing **joint tracking** of many points. CoTracker is a **transformer-based model that tracks many 2D points in extended video sequences**. Unlike previous methods that typically track points independently, CoTracker introduces the concept of **joint tracking**. This means paying attention to dependencies between tracked points, leading to enhanced accuracy and robustness, mainly when dealing with occlusions or points moving out of the camera view. ## Introducing CoTracker3 Building upon the foundation laid by CoTracker, [**CoTracker3 has a simpler architecture, improved data efficiency, and greater flexibility**.](https://cotracker3.github.io/) While maintaining the core concept of joint point tracking, CoTracker3 refines and streamlines various aspects, achieving state-of-the-art results with significantly less training data. Here are some of the **improvements and innovations** introduced by CoTracker3 that I feel are interesting: ### **Architectural Simplifications and Enhancements** ### **Semi-Supervised Training Pipeline** ### **Performance Improvements** ## **Limitations** Despite its advancements, CoTracker3, like its predecessor, has its limitations: CoTracker and CoTracker3 are significant point-tracking advancements, mainly when dealing with long video sequences and challenges like occlusions. CoTracker introduced the innovative concept of joint tracking through a transformer network, achieving state-of-the-art performance but relying heavily on synthetic training data. CoTracker3 builds upon this foundation, introducing architectural simplifications, a novel semi-supervised training pipeline, and improved efficiency. By leveraging multiple pre-trained trackers as teachers to generate pseudo-labels for real-world videos, CoTracker3 significantly reduces the dependency on synthetic data. It achieves even better accuracy with a thousandfold reduction in real data requirements. Both models highlight the power of considering dependencies between tracked points and utilizing context to enhance tracking accuracy. ## **Next Steps** Now that you’re up to speed on the task of point tracking and the CoTracker3 model, it’s time to get hands-on with some code! Check out [this blog](/content/blog/cotracker3-a-point-tracker-using-real-videos/index.html) where you’ll learn about: - Online vs. Offline Modes - Running Inference - Visualizing Results - FiftyOne Integration - Memory Management [CoTracker3](/content/blog/tag/cotracker3/index.html) [point tracking](/content/blog/tag/point-tracking/index.html)