How to use interpolation for video annotation: a comprehensive guide
Video annotation is a pillar in preparing the data needed to train artificial intelligence models. In fields like Computer Vision, this process can quickly become tedious, especially when it comes to processing long video sequences with numerous frames (Bounding Box, key points, polygons, etc.) to be annotated manually. In this article, we explain to you how thevideo interpolation - a technique embedded in most of modern annotation tools - facilitates the work of preparing and annotating data.
Interpolation is a partial automation method to make annotation tasks more efficient. Using interpolation, only a few key frames require manual annotation as a Ground Truth. The annotation tool algorithm is then responsible for propagating the labels to Frames sequential, which speeds up the process while ensuring the consistency and precision of the annotations. It is a technical method, which does not make the work of data annotation obsolete: on the contrary, this method requires rigor and expertise on the part of Data Labelers. In other words, by using interpolation, you are professionalizing your data annotation workflows!
The interpolation technique for video annotation is particularly beneficial in sectors such as autonomous driving, surveillance, and health, where the need for annotated data is critical for the training of machine learning models. In this guide, as usual, we explain the basics and everything you need to know before embarking on a project to process large volumes of video data.
Introduction: what is video annotation in AI?
Video annotation is a process for creating video datasets to provide high-quality data to train machine learning models. By adding annotations to videos (or labels), artificial intelligence algorithms can better understand and interpret visual information, which is essential for a variety of applications ranging from object recognition to the detection of complex movements. Video annotations play a fundamental role in creating accurate and reliable databases (and metadata), essential for the development of efficient artificial intelligence systems.
Definition of video annotation
Video annotation is the process of adding labels to videos to provide additional information about the objects, events, and actions that occur in the video. These annotations can take a variety of forms, such as bounding boxes, polygons, key points, or even text segments. They make it possible to precisely describe the elements present in each frame, thus facilitating the analysis and interpretation of data by machine learning algorithms. Annotating videos creates information-rich data sets that are essential for training models that can perform complex tasks for Computer Vision algorithms, for example.
Importance of video annotation in machine learning
Video annotation is essential in machine learning because it provides high-quality data to train machine learning models. For example, in autonomous driving, annotations allow vehicles to detect and respond to pedestrians, other vehicles, and traffic signs. In surveillance, they help identify and track individuals or objects of interest.
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What is interpolation in video annotation?
Interpolation in video annotation is a technique used to speed up the process of manual marking of objects in a video sequence. Instead of annotating each image individually, interpolation allows annotators to mark a few key frames, and an algorithm then takes care of propagate these annotations through the successive images.
This method is based on the fact that objects in videos often move fluidly between successive frames. So, if an object is properly annotated in a first image (Key image) and in a later image, the algorithm can predict its position and shape in the images located between these two points.
This reduces manual workload, especially for long videos or objects that evolve slowly, while ensuring consistency in tracking objects.
There are various interpolation methods, such as linear interpolation, which follows a straight path between two key frames, or more advanced methods based on artificial intelligence models that analyze complex variations in objects or scenes. Later on, in this article, we give you an overview of these main methods...
Interpolation is particularly useful in sectors that require large amounts of annotated data, such as autonomous driving, video surveillance, or computer vision research projects.
While interpolation speeds up the annotation process, it is not without limitations. Annotators still need to check and adjust the annotations to ensure the quality of the predictions, especially in cases where objects change shape or trajectory unpredictably.
What you need to remember: definition of interpolation in video annotation
Interpolation is a technique used in video annotation to estimate missing values between Frames of a video. Instead of annotating each Frame individually, interpolation allows you to create annotations for Frames intermediaries based on a few key images annotated manually. This method significantly reduces the time and costs associated with video annotation, while maintaining high consistency and accuracy. By using interpolation, annotators can focus on key frames, while the algorithm propagates these annotations to Frames intermediaries, thus facilitating the annotation process.
How does interpolation make video annotation easier?
Interpolation makes video annotation easier by significantly reducing the time and effort required to manually annotate each frame in a video sequence. Here are the main ways she is improving the process:
Reduction in manual work
Instead of annotating each frame in a video, annotators can focus on a few key frames, called Keyframes. Interpolation uses these annotations to predict and propagate markings to middle frames, eliminating the need for frame-by-frame annotation. This saves a lot of time, especially for long video sequences. However, the method for using interpolation deserves to be clarified in advance, as soon as you develop your annotation strategy and manual...
Seamless object tracking
Interpolation makes it possible to automatically track objects between key frames, which ensures continuity and consistency in the annotation. Algorithms can track moving objects taking into account their trajectory and visual variations, even when the object changes position or shape slightly.
Productivity improvement
By reducing the number of images that need to be annotated manually, interpolation greatly increases the productivity of annotators. This is especially beneficial in areas that require complex annotations, such as autonomous driving, where video data is massive and needs to be processed quickly to form artificial intelligence models.
Algorithm flexibility
Modern annotation tools incorporate advanced interpolation algorithms that can handle various types of objects and movements. For example, interpolation can be linear or rely on machine learning models to deal with more complex or non-linear movements.
Does interpolation affect the precision of annotations?
Interpolation can affect the accuracy of annotations, although this depends on a number of factors. Here are a few things to consider:
Key image quality
The accuracy of the interpolated annotations depends heavily on the quality of the selected keyframes. If the objects are properly annotated in these images, the interpolation between the keyframes can be quite accurate.
However, if key frames are incorrectly selected or annotated in an approximate manner, interpolation risks spreading these errors through the intermediate images, thus reducing the overall quality of the annotations.
Complexity of movements
Interpolation works well for objects that move in a linear or predictable manner, but it may be less accurate in cases where objects suddenly change direction, shape, or speed.
In these situations, the interpolation algorithm may struggle to keep up with complex movements, resulting in incorrect annotations that will require manual adjustments.
Interpolation algorithms used
More basic algorithms, like linear interpolation, are less accurate in scenarios where object movements are nonlinear or irregular.
In contrast, interpolation algorithms based on artificial intelligence can better manage these variations by analyzing the visual characteristics of objects, which improves accuracy, even for complex movements. In addition, the segmentation can be used to break images into smaller segments, improving the accuracy of annotations.
Manual checks
Even with advanced interpolation, it is often necessary to manually check the results and make corrections in some images. This is especially true when objects interact, overlap, or disappear temporarily in the video. If these checks are not done, accuracy may be affected. You don't have the expertise to perform manual checks on your annotated video data? Do not hesitate to contact us!
How do you combine interpolation and object tracking to improve results?
To effectively combine interpolation and object tracking in order to improve video annotation results, several strategies can be implemented:
Use interpolation to reduce initial workload
Interpolation can be used to automatically mark the middle frames between two keyframes. This eliminates the need to annotate each image individually. The advantage is that it provides a solid basis for predictions, which object tracking can then refine.
In other words, interpolation creates a “skeleton” based on annotations, on which object tracking relies to adjust predictions based on complex movements.
Apply object tracking for dynamic adjustments
Object tracking, especially if it is based on artificial intelligence, allows the annotations of an object to be automatically adjusted as it moves in the video. Tracking models analyze the visual characteristics of the object (such as contours, colors, and textures) and can correct errors or anomalies left by interpolation.
For example, if an object changes shape or orientation, object tracking detects these changes and adapts the annotations, while interpolation alone could be inaccurate in these cases.
Refining key images
When interpolation is combined with object tracking, key frames can be better selected. The object tracking algorithm makes it possible to suggest frames where manual adjustments are needed, for example at points where the object's trajectory becomes unpredictable or where the object interacts with other objects.
This allows manual efforts to be concentrated only on Frames critical, thus optimizing the time spent on the validation of annotations.
Joint use to correct propagation errors
A combination of the two methods helps correct common errors in interpolation, especially when objects overlap or temporarily go out of frame.
Object tracking, thanks to its ability to “understand” movements based on visual characteristics, can correct these errors and thus improve the accuracy of annotations throughout the video.
Hybrid automation
In modern tools like V7 Labs and LabelBox, interpolation and object tracking can be combined in a hybrid workflow. Interpolation is used to generate quick annotations in areas of linear or regular motion, while object tracking takes care of more complex areas. This makes it possible to process large amounts of video data while reducing the need for manual intervention.
How do I correct the errors generated by automatic interpolation?
Correcting errors generated by automatic interpolation in video annotation is an essential step in ensuring accurate and high-quality annotations. Here are several ways to correct these errors:
Identifying errors in keyframes
A first check consists in inspecting the key frames used for interpolation. If these keyframes are incorrectly annotated or do not represent the object or movement correctly, they can cause errors in the intermediate images.
In this case, it is necessary to manually readjust the annotations in these key frames, which allows the interpolation algorithm to recalculate the intermediate images more accurately.
Add additional keyframes
If interpolation fails to accurately track an object, especially when there are rapid or complex changes in the object's motion or shape, adding additional keyframes can help improve accuracy.
By adding more frequent reference points, the interpolation algorithm can better capture motion details and reduce errors generated between existing keyframes.
Use object tracking techniques
In addition to interpolation, the use of object tracking techniques (Object tracking) can help correct interpolation errors. Tracking algorithms analyze the visual characteristics of objects (such as outlines, colors, and textures) and can adjust annotations where automatic interpolation has failed.
Object tracking makes it possible to correct annotations in frames where the movements are more complex or irregular. Additionally, cuboids can be used to annotate objects in 3D point clouds, improving the accuracy of annotations.
Manual verification of problem frameworks
While interpolation speeds up the process, manual frame checks are often required to identify and correct errors. This involves reviewing the interpolated images and manually adjusting the annotations if the object has not been properly followed or if anomalies occur, especially when there are sudden changes in the object's movement.
Use of more advanced algorithms
If errors persist, it may be useful to use more sophisticated interpolation algorithms based on artificial intelligence. These algorithms can analyze the characteristics of objects more finely and better predict their behavior in intermediate frames, which reduces automatic annotation errors.
💡 By combining these approaches, errors generated by automatic interpolation can be corrected effectively, allowing for more accurate and better annotations in video annotation projects.
How do I choose keyframes for video interpolation?
Choosing keyframes for video interpolation is an essential step in ensuring the accuracy and quality of automatic annotations. Here are several factors to consider when selecting the best keyframes:
- Significant changes in the scene : It is important to choose key frames where there are significant visual changes, such as changes in the position, size, or shape of an object. For example, when an object starts or finishes moving, or when it changes direction. This allows interpolation to adapt to major variations in the sequence.
- Frames representing the extremes of a movement : When tracking moving objects, select key frames that represent the extreme positions of the movement. This allows the interpolation algorithm to create a smooth transition between these points and better capture the trajectory.
- Complex transitions : If the object changes in appearance rapidly (for example, due to the angle of view, shadows, or light conditions), choose key frames around these transitions. This will allow variations in the shape or color of the object to be captured more accurately.
- Points of intersection or overlap : If several objects interact or overlap in the video, it is a good idea to choose key frames before and after these interactions. This ensures that the interpolation algorithm does not make mistakes in tracking objects.
- Regular key frame spacing : In general, it is recommended to choose keyframes spaced enough apart to cover the entire movement of an object without relying too much on interpolation. Regular spacing reduces the risk of significant errors in predictions between two frames.
- Interpolation errors detected : After an initial interpolation phase, annotators may notice errors in certain parts of the sequence. In these cases, it is useful to select additional keyframes to correct these errors, by manually adding annotations to problem frames.
💡 By combining these approaches, it is possible to reduce the number of images that need to be annotated manually while maintaining high quality in the interpolated annotations.
What types of interpolation algorithms are used in video annotation?
In video annotation, several types of interpolation algorithms are used to automate the generation of annotations between key frames. Here is a non-exhaustive list of these algorithms:
- Linear interpolation : It is one of the simplest and most used methods. It consists in drawing a straight line between two key images and in adjusting the position of the objects in the intermediate images according to this trajectory. While this approach is effective for simple or straight line movements, it is less effective for complex or irregular movements.
- Spline interpolation : Unlike linear interpolation, spline interpolation uses curves to generate smoother trajectories between key frames. This makes it possible to better track objects with complex, irregular, or directional movements.
- AI-based interpolation (deep learning models) : These algorithms use artificial intelligence models to predict the movement and shape of objects between key frames based on existing manual annotations. These models learn from data and can better manage non-linear movements, changes in shape or perspective, and changing lighting conditions.
- Interpolation by visual characteristics : This method uses algorithms to analyze the visual characteristics of objects, such as contours or textures, and to follow them in the intermediate images. It is particularly effective when objects change shape or are partially hidden in some images.
- Interpolation by polygonal morphing : Used for annotations with polygons, this method adjusts the shape of objects between keyframes based on changes observed in the polygon's control points. This is useful for tracking objects that have changing contours or irregular shapes, such as people or animals.
💡 These algorithms are chosen according to the specificities of the data to be annotated (movement, type of object) and the needs of the annotation project, in particular in terms of precision and speed.
What open source tools allow interpolation to be used for video annotation?
There are several open source tools that allow interpolation to be used for video annotation. Here are some popular examples:
CVAT (Computer Vision Annotation Tool)
CVAT is an open source tool that is widely used for video and image annotation. It includes interpolation to speed up the annotation process, especially for videos with moving objects. The tool allows annotators to tag a few key frames and use interpolation to track these objects in middle frames.
CVAT supports annotation with bounding boxes, polygons, key points, and more. Below is an overview of the features for interpolating polygons between multiple frames, using CVAT (source: CVAT)
LabelImg
Although originally designed for image annotation, LabelImg supports advanced features like interpolating annotations when working with image sequences extracted from videos. This allows users to annotate moving objects in videos more effectively.
Scalabel
Another open source tool that offers interpolation functionality for video annotation. Scalabel is designed for Computer Vision projects, and interpolation reduces manual annotation efforts by automatically generating annotations for images that are intermediate between two key images.
These open source tools are particularly suitable for projects that require large amounts of annotated data, such as in the fields of autonomous driving, surveillance, and medical research. They make it possible to speed up the annotation process while guaranteeing good precision thanks to the use of sophisticated interpolation algorithms.
In which sectors is video annotation interpolation most used?
Interpolation in video annotation is used in several industries where the analysis of large amounts of video data is essential. Here are some of the sectors where this technique is most common:
Autonomous driving
In the development of autonomous vehicles, it is necessary to annotate massive video footage to train computer vision systems that can detect and track objects such as pedestrians, vehicles, and traffic signs. Interpolation makes it possible to quickly process these sequences and to reduce the costs associated with manually annotating each video.
Surveillance and security
AI-based surveillance systems use cameras to analyze video streams in real time. Interpolation is particularly useful for annotating objects such as people or vehicles in long sequences, especially for tracking movements in complex environments such as shopping centers or airports.
Health and medical research
In healthcare, videos are often used to analyze medical procedures or exams such as endoscopy or surgery videos. Interpolation reduces the annotation time required to track the movements of surgical tools or to mark visible anomalies in medical videos.
Drones and aerial surveillance
Drones capture vast video footage, often over long distances. Interpolation is essential for annotating the movements of objects, such as vehicles or infrastructure, in aerial surveillance videos, for example to monitor traffic or analyze disaster areas.
Retail industry
Retailers are starting to use AI-based cameras to analyze consumer behavior in stores. Interpolation makes it possible to track customer movements across different areas of a store, thus facilitating valuable analyses to optimize shelf layouts or sales strategies.
In conclusion
Interpolation in video annotation is a powerful method for reducing the time and effort associated with manual annotation, while maintaining a good level of accuracy. Whether it is linear interpolation for simple movements or more sophisticated approaches such as spline interpolation and AI-based techniques, these methods make it possible to automatically generate annotations on intermediate images between two key images pre-selected by specialists in data labeling processes. Combined with expertise in annotation processes for AI, video interpolation facilitates the work of annotators and, above all, makes it more efficient and qualitative.
However, the quality of annotations generated using video interpolation techniques depends on the accuracy of the key frames chosen, and manual verification is often necessary to correct errors in complex movements or changes in appearance. Thus, by combining interpolation techniques with advanced object tracking tools and the expertise of specialized teams, it is possible to maximize the speed and precision of annotation, while meeting the requirements of complex projects in sectors such as autonomous driving, surveillance, and medical research.
The integration of these approaches not only makes it possible to gain in productivity, but also to produce high quality data sets, essential for training artificial intelligence models!
