Video annotation is a cornerstone of data preparation for training artificial intelligence models. In fields such as Computer Vision, this process can quickly become laborious, particularly when dealing with long video sequences with numerous frames (bounding box, key points , polygons, etc.) to be manually annotated. In this article, we explain how video interpolation - a technique embedded in most modern annotation tools - makes data preparation and annotation easier.
Interpolation is a partial automation method for making annotation tasks more efficient. Using interpolation, only a few key images require manual annotation as a means of Ground Truth. The annotation tool's algorithm then propagates the labels to successive frames , speeding up the process while ensuring annotation consistency and accuracy. It's a technical method, which doesn't make data annotation work obsolete: on the contrary, it requires rigor and expertise on the part of Data Labelers. In short, by using interpolation, you can professionalize your data annotation workflows!
The interpolation technique for video annotation is particularly beneficial in sectors such as autonomous driving, surveillance and healthcare, where the need for annotated data is decisive 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 the process of creating video datasets to provide high-quality data for training 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 complex motion detection. Video annotations play a fundamental role in the creation of accurate and reliable databases (and metadata), essential for the development of high-performance artificial intelligence systems.
Definition of video annotation
Video annotation is the process of adding labels to videos to provide additional information about objects, events and actions occurring in the video. These annotations can take various forms, such as bounding boxes, polygons, key points or even text segments. They enable a precise description of the elements present in each frame, making it easier for machine learning algorithms to analyze and interpret the data. By annotating videos, we create information-rich datasets, essential for training models capable of performing complex tasks for Computer Vision algorithms, for example.
Importance of video annotation in machine learning
Video annotation is essential in machine learning, as it provides high-quality data for training machine learning models. For example, in autonomous driving, annotations enable vehicles to detect and react 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 manually manual marking of objects in a video sequence. Rather than annotating each frame individually, interpolation enables annotators to mark a few key frames, and an algorithm then propagates these annotations across successive frames.
This method is based on the fact that objects in videos often move fluidly between successive images. So, if an object is correctly annotated in a first frame(key frame) and in a subsequent frame, the algorithm can predict its position and shape in the frames between these two points.
This reduces the manual workload, particularly for long videos or slow-moving objects, while ensuring consistency in object tracking.
There are various interpolation methods, such as linear interpolation, which follows a straight path between two key images, or more advanced methods based on artificial intelligence models that analyze complex variations in objects or scenes. Later in this article, we give you an overview of these main methods...
Interpolation is particularly useful in sectors that require large quantities of annotated data, such as autonomous driving, video surveillance and computer vision research projects.
Although interpolation speeds up the annotation process, it is not without its limitations. Annotators still need to check and adjust annotations to ensure the quality of predictions, particularly in cases where objects change shape or trajectory unpredictably.
What you need to know: definition of interpolation in video annotation
Interpolation is a technique used in video annotation to estimate missing values between frames in a video. Rather than annotating each frame individually, interpolation creates annotations for intermediate frames based on a few manually annotated key frames. This method considerably reduces the time and costs associated with video annotation, while maintaining high consistency and accuracy. By using interpolation, annotators can concentrate on key frames, while the algorithm takes care of propagating these annotations to intermediate frames , thus facilitating the annotation process.
How does interpolation facilitate video annotation?
Interpolation facilitates video annotation by significantly reducing the time and effort required to manually annotate each frame of a video sequence. Here are the main ways in which it improves the process:
Reducing manual labor
Instead of annotating every frame of a video, annotators can concentrate on a few key frames, called keyframes. Interpolation uses these annotations to predict and propagate markings to intermediate frames, eliminating the need for frame-by-frame annotation. This saves a considerable amount of time, especially for long video sequences. However, the method of using interpolation needs to be clarified in advance, as soon as you draw up your annotation strategy and manual. your annotation strategy and manual...
Smooth object tracking
Interpolation automatically tracks objects between key images, ensuring continuity and consistency in 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 to be manually annotated, interpolation significantly increases annotator productivity. This is particularly advantageous in fields requiring complex annotations, such as autonomous driving, where video data is massive and needs to be processed rapidly to train artificial intelligence models.
Algorithm flexibility
Modern annotation tools incorporate advanced interpolation algorithms, capable of handling different types of objects and movements. For example, interpolation can be linear, or rely on machine learning models to handle more complex or non-linear movements.
Does interpolation affect annotation accuracy?
Interpolation can affect the accuracy of annotations, although this depends on a number of factors. Here are some points to consider:
Key image quality
The accuracy of interpolated annotations is highly dependent on the quality of the key images selected. If objects are correctly annotated in these images, interpolation between key images can be fairly accurate.
However, if the key images are poorly selected or annotated in an approximate manner, interpolation risks propagating these errors through the intermediate images, thus reducing the overall quality of the annotations.
Movement complexity
Interpolation works well for objects that move linearly or predictably, but can 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, such as linear interpolation, are less accurate in scenarios where object movements are non-linear or irregular.
Artificial intelligence-based interpolation algorithms, on the other hand, can better handle these variations by analyzing the visual characteristics of objects, thus improving accuracy, even for complex movements. In addition segmentation can be used to divide images into smaller segments, improving annotation accuracy.
Manual checks
Even with advanced interpolation, it is often necessary to manually check the results and make corrections in certain images. This is particularly true when objects interact, overlap or temporarily disappear in the video. If these checks are not carried out, accuracy may be affected. You don't have the expertise to carry out manual checks on your annotated video data? Don't hesitate to contact us!
How to combine interpolation and object tracking to improve results?
To effectively combine interpolation and object tracking to improve results in video annotation, several strategies can be implemented:
Use interpolation to reduce initial workload
Interpolation can be used to automatically mark intermediate frames between two key images. This eliminates the need to annotate each frame individually. The advantage is that it provides a solid base of predictions, which object tracking can then refine.
In other words, interpolation creates a basic "skeleton" of annotations, on which object tracking relies to adjust predictions according to complex movements.
Apply object tracking for dynamic adjustments
Object tracking, especially when based on artificial intelligence, can automatically adjust an object's annotations as it moves through the video. Tracking models analyze the object's visual characteristics (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 annotations, whereas interpolation alone may lack precision in these cases.
Key image refinement
When interpolation is combined with object tracking, it is possible to better select key frames. The object tracking algorithm can suggest frames where manual adjustments are required, 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 solely on critical frames , optimizing the time spent on validating annotations.
Joint use to correct propagation errors
A combination of the two methods helps to correct common interpolation errors, such as 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 such as V7 Labs and Labelboxinterpolation and object tracking can be combined in a hybrid workflow. Interpolation is used to generate fast annotations in areas of linear or regular motion, while object tracking takes care of more complex areas. This makes it possible to process large quantities of video data while reducing the need for manual intervention.
How to correct errors generated by automatic interpolation?
Correcting errors generated by automatic interpolation in video annotation is an essential step in ensuring accurate, high-quality annotations. Here are several methods for rectifying these errors:
Identifying errors in key images
A first check is to inspect the keyframes used for interpolation. If these keyframes are poorly annotated or do not correctly represent the object or motion, they can lead to errors in the intermediate images.
In this case, it is necessary to manually readjust the annotations in these key images, enabling the interpolation algorithm to recalculate the intermediate images more accurately.
Add additional key images
If interpolation fails to accurately track an object, particularly when there are rapid or complex changes in object movement 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 the errors generated between existing keyframes.
Use object tracking techniques
In addition to interpolation, the use ofobject tracking techniques can help correct interpolation errors. Object tracking algorithms analyze the visual characteristics of objects (such as contours, colors and textures) and can adjust annotations where automatic interpolation has failed.
Object tracking can be used to correct annotations in frames where movements are more complex or irregular. In addition, cuboids can be used to annotate objects in 3D point clouds, improving annotation accuracy.
Manual verification of problem frames
Although interpolation speeds up the process, it is often necessary to manually check frames to identify and correct errors. This involves reviewing interpolated images and manually adjusting annotations if the object has not been tracked correctly, or if anomalies appear, particularly during abrupt changes in object 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 object characteristics more finely and better predict their behavior in intermediate frames, thus reducing automatic annotation errors.
💡 By combining these approaches, errors generated by automatic interpolation can be effectively corrected, enabling more accurate and higher quality annotations in video annotation projects.
How to select key frames for video interpolation?
Choosing keyframes for video interpolation is an essential step in guaranteeing the accuracy and quality of automatic annotations. Here are several factors to consider when selecting the best keyframes:
- Significant changes in the scene: It's important to choose keyframes where there are significant visual changes, such as modifications to the position, size or shape of an object. For example, when an object starts or finishes moving, or when it changes direction. This enables interpolation to adapt to major variations in the sequence.
- Frames representing the extremes of movement: When tracking moving objects, select key frames that represent the extreme positions of movement. This enables the interpolation algorithm to create a smooth transition between these points and better capture the trajectory.
- Complex transitions: If the object changes appearance rapidly (for example, due to viewing angle, shadows or lighting conditions), choose keyframes around these transitions. This will enable you to capture variations in the object's shape or color more accurately.
- Intersection or overlap points: If several objects interact or overlap in the video, it's a good idea to choose key frames before and after these interactions. This ensures that the interpolation algorithm doesn't make any mistakes when tracking objects.
- Regular spacing of key frames: In general, it is recommended to choose key frames spaced far enough apart to cover the entire motion of an object without relying too heavily on interpolation. Regular spacing reduces the risk of large 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 such cases, it is useful to select additional key frames to correct these errors, by manually adding annotations to the problem frames.
💡 By combining these approaches, it is possible to reduce the number of images to be manually annotated while maintaining high quality in interpolated annotations.
What kind 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: This is one of the simplest and most widely used methods. It involves drawing a straight line between two key frames and adjusting the position of objects in the intermediate frames 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 easier to track objects with complex, irregular movements or changing direction.
- AI-based interpolation (deep learning models): These algorithms use artificial intelligence models to predict the movement and shape of objects between key images based on existing manual annotations. These models learn from the data and can better handle non-linear movements, changes in shape or perspective, as well as changing lighting conditions.
- Interpolation by visual characteristics: This method uses algorithms to analyze the visual characteristics of objects, such as contours or textures, and track them in intermediate images. It is particularly effective when objects change shape or are partially masked in certain images.
- Polygon morphing interpolation: Used for annotations with polygons, this method adjusts the shape of objects between keyframes according to changes observed in the polygon's control points. This is useful for tracking objects with changing contours or irregular shapes, such as people or animals.
💡 These algorithms are chosen according to the specifics of the data to be annotated (movement, object type) and the needs of the annotation project, particularly in terms of accuracy and speed.
What open source tools are available for using interpolation for video annotation?
Several open source tools allow you to use interpolation for video annotation. Here are a few popular examples:
CVAT (Computer Vision Annotation Tool)
CVAT is a widely used open source tool for video and image annotation. It incorporates interpolation to speed up the annotation process, particularly for videos with moving objects. The tool allows annotators to mark a few key frames and use interpolation to track these objects in intermediate frames.
CVAT supports annotation with bounding boxes, polygons, key points and more. Below, an overview of polygon interpolation functionality between multiple frames, using CVAT(source: CVAT)
LabelImg
Although originally designed forimage annotationLabelImg supports advanced features such as annotation interpolation when working with image sequences extracted from videos. This enables users to annotate moving objects in videos more effectively.
Scalabel
Another open source tool offering interpolation functionality for video annotation. Scalabel is designed for Computer Vision projects, and interpolation reduces manual annotation efforts by automatically generating annotations for intermediate frames between two key frames.
🪄 These open source tools are particularly suited to projects requiring large quantities of annotated data, such as in the fields of autonomous driving, surveillance and medical research. They speed up the annotation process while guaranteeing good accuracy through the use of sophisticated interpolation algorithms.
In which sectors is video annotation interpolation most widely used?
Interpolation in video annotation is used in several sectors where the analysis of large quantities of video data is essential. Here are some of the sectors where this technique is most widespread:
Autonomous driving
In the development of autonomous vehicles, it is necessary to annotate massive video sequences to train computer vision systems capable of detecting and tracking objects such as pedestrians, vehicles and traffic signs. Interpolation enables these sequences to be processed rapidly, reducing the cost of 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 malls or airports.
Health and medical research
In the healthcare sector, videos are often used to analyze medical procedures or examinations such as endoscopy or surgical videos. Interpolation reduces the annotation time needed to track the movements of surgical tools or to mark visible anomalies in medical videos.
Drones and aerial surveillance
Drones capture large video sequences, 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 beginning to use AI-based cameras to analyze consumer behavior in-store. Interpolation makes it possible to track customer movements through different areas of a store, facilitating valuable analyses to optimize shelf layout or sales strategies.
In conclusion
Interpolation in video annotation is a powerful method for reducing the time and effort involved in manual annotation, while maintaining a good level of accuracy. From linear interpolation for simple movements to more sophisticated approaches such as spline interpolation and AI-based techniques, these methods can automatically generate annotations on intermediate images between two key frames 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 still required to correct errors in complex movements or changes in appearance. So, by combining interpolation techniques with advanced object tracking tools and the expertise of specialized teams, it is possible to maximize the speed and accuracy of annotation, while meeting the requirements of complex projects in sectors such as autonomous driving, surveillance and medical research.
Integrating these approaches not only boosts productivity, but also produces high-quality data sets, essential for training artificial intelligence models!
