Track Mode in CVAT is a video annotation tool that helps annotate moving objects faster and more conveniently. Instead of manually labeling each frame, annotators can define key points, and the system will automatically fill in the intermediate frames.

Interpolation

Track Mode uses interpolation. Interpolation is a method of automatically generating intermediate frames between annotated keyframes. In the context of CVAT, this means that an annotator marks an object in several keyframes, and the system automatically fills in the object's positions in the remaining frames. CVAT supports linear interpolation, where the object moves in a straight line between two key points.

Where is Interpolation Used?

Interpolation is useful in various computer vision tasks, especially for video annotation. Some key applications include:

• Autonomous Vehicles – Tracking pedestrians, vehicles, and road signs. Interpolation helps create high-quality datasets for training neural networks that enable vehicles to predict object movements on the road.
  
• Surveillance and Security – Tracking people, cars, and other moving objects in real time. This is crucial for detecting suspicious activities, analyzing crowd behavior, and monitoring traffic.
  
• Sports Analytics – Automated analysis of athletes’ movements and game objects (balls, pucks), which is used for tactical analysis and team strategy development.
  
• Medical Applications – Tracking organ movements, surgical instruments, and analyzing videos from endoscopic and radiological studies.
  

Advantages and Disadvantages of Interpolation

Advantages

• Time Savings – Interpolation significantly reduces annotation time since annotators don't have to mark every frame manually.
  
• Smooth Tracking – The transition between frames is fluid, making object movement appear natural, especially in long video sequences.
  
• Reduced Workload for Annotators – Using interpolation minimizes repetitive tasks, allowing specialists to focus on more complex aspects of annotation.
  
• Improved Annotation Consistency – Automatic filling of intermediate frames reduces discrepancies between sequential annotations.
  
• Support for Complex Scenes – When combined with manual adjustments, interpolation helps annotate scenes with multiple moving objects, which is especially important for surveillance and autonomous driving.
  

Disadvantages

• Errors in Complex Movements – If an object changes trajectory sharply or its speed fluctuates, linear interpolation may result in inaccurate annotations.
  
• Issues with Shape Changes – Standard interpolation methods are not well-suited for objects that change shape (e.g., a person raising their hand or a bending object).
  
• Need for Validation – Automatic interpolation does not always produce perfect results, so annotators must manually review and adjust annotations, which can take additional time.
  

Popular Datasets with Interpolated Objects

• KITTI Tracking – One of the most popular datasets for automotive computer vision, containing annotated frame sequences with various moving objects.
  
• AICity Challenge – Includes videos from urban surveillance cameras, used for traffic analysis and road safety applications.
  
• BDD100K – Provides annotated road object videos in various weather and lighting conditions, useful for autonomous driving tasks.
  
• TAO (Tracking Any Object) – A dataset designed for tracking various types of objects in videos, supporting annotation interpolation.
  

Conclusion

Track Mode in CVAT is a powerful video annotation tool that speeds up the annotation process through interpolation. However, like any automated method, it requires validation and adjustment. Interpolation-based annotation is widely used in fields such as autonomous driving, surveillance, and sports analytics, making it a valuable tool in computer vision.