Video annotation projects: what are the most common mistakes?
💡 Video annotation projects: learn from the most common mistakes to train powerful models!
Getting started with data annotation for AI is one thing when it comes to just images. It's pretty simple — a bit of focus, patience, and everyone can pretend image annotator. Now, move on to theVideo annotation, and you're playing in a totally different league.
Let's say you have a 1-minute video clip. If the framerate is 24 frames per second, which is fairly standard in video, you have to manage 1,440 frames. Imagine that — 1,440 distinct moments to label, each representing a “piece” of the action. This greatly increases the complexity of your annotation task. And these are optimized videos... I have not yet mentioned the case of medical videos for example, the noise or the low resolution, which are as many complexity factors in video annotation.
Mastering video annotation is more than patience and attention to detail. You need real know-how and a strategy for analysis and content moderation sturdy. Luckily, we're here to help! With a few expert tips, qualified annotators, and tips from our experience, you'll be mastering video editing and annotation for your AI needs in no time.
Introduction to video annotation in artificial intelligence (AI)
Data annotation (including video annotation) is a critical component in the process of training algorithms to accurately understand and interpret visual information. The annotation is the Data language. Video annotation involves the object labeling, actions, and events in video images, allowing AI models to recognize and respond appropriately to these elements. In this article, we explore the field of video annotation to understand its importance in the process of building high-performance AI products, as well as The most common pitfalls associated with video tagging.
The field of data annotation (including video annotation) is the labeling of data and the annotation of videos that classify data to teach computers to recognize and differentiate various objects or scenarios. Let's take a simple example: a data set full of images of cats and dogs. By annotating each image with the right labels, we are setting the stage for a computer vision model to accurately identify whether a given image or video contains a cat or a dog, or multiple cats or dogs.
Annotating data is a critical task when it comes to creating AI solutions that work in the real world. It is the cornerstone of AI models, the language of data in supervised learning models, where AI learns from examples. Adding labels or masks to video content can be done by hand (with manual annotations, done internally, by your team, or by a service provider) or, to some extent, via automated systems. Labels can be as simple as marking objects in a frame or as nuanced as identifying complex actions and emotions.
The applications of AI video tagging are diverse. Here are 4 applications:
1. Object detection
This involves training the AI to spot specific items in video footage. For example, identify and trace objects, detect vehicles, spot road damage, or identify animals in a video.
2. Object tracking
Here, the AI is trained to follow objects through video sequences, predicting their next moves. This ability is critical for tasks such as monitoring pedestrians or monitoring vehicle movements for safety reasons.
3. Locating objects
The AI is trained to Locate objects in video footage and provide their contact information. This is a useful feature for managing aspects such as the ability of a vehicle to park independently.
4. Categorization/Segmentation
This involves categorize objects in the video. For example, an AI model could be trained to differentiate humans and vehicles in a video, categorizing them accordingly.
💡 By annotating data, we're essentially teaching our AI models to focus on what matters. The final objective is to create training data and to have the models automatically recognize the patterns, to allow it to automatically label new data after a period of learning.
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Methods used in video annotation for AI: a brief overview
AI and data specialists, such as data annotators, use multiple video annotation methods. The correct method depends on the type of annotated video and the use of the tagged data. Each video annotation tool and method meets specific types of video training data and project requirements, offering unique benefits and challenges.
Here is a list of various annotation methods for videos:
Enclosing boxes: at the heart of object recognition
Les encompassing boxes are a fundamental video annotation method where annotators draw rectangles around objects. This technique is simple and particularly effective for simple, well-defined objects and is widely used due to its simplicity and cost effectiveness.
Semantic segmentation: revealing details with masks
La semantic segmentation breaks objects down into their individual components. This detailed method involves labeling each part of an object separately, allowing AI systems to distinguish and analyze components of the same object in complex scenes.
Key point annotation: highlight specific aspects
In theAnnotating key points, specific points or marks on an object are marked. This technique is particularly useful in applications such as facial recognition or motion analysis, where pinpointing precise locations is essential to identify objects.
3D cuboid annotation: add an extra dimension
THE3D cuboid annotation adds a dimension of depth, which is essential for understanding objects in a spatial context. This method is instrumental in applications involving spatial awareness, such as autonomous vehicle navigation and robotics.
Polygonal annotation: capturing complex contours
For irregularly shaped objects, thepolygonal annotation offers a more precise solution than enclosing boxes. By drawing polygons (multi-sided shapes) around objects, this method provides a precise outline that is essential for complex shapes.
Annotating landmarks: precision in detail
Similar to annotating key points, theAnnotating landmarks focuses on labeling specific points to identify specific objects in video frames. It is very effective for detailed analyses, such as facial recognition or the biometric identification of objects as well.
Object tracking annotation: follow the movement
The object tracking involves annotating objects across consecutive video frames together, helping machine learning models track movement and predict future positions. This method of training machine learning models on image data is vital in surveillance, object detection, and surveillance applications.
Annotation by interpolation: smooth transitions
THEannotation by interpolation uses key frames to predict and fill in annotations in intermediate images. This is especially useful in videos where objects move in a predictable manner, reducing the workload of annotation frame by frame.
Video classification: categorize entire clips
In the Video classification, entire video files, or clips are tagged according to their content or overall theme. This high-level annotation helps AI systems understand and categorize videos, such as distinguishing between genres in movies or video archives.
What is the benefit of annotating videos instead of images?
The videos consist of sequences of Frames, but annotate them as full videos rather than Frames Isolated offers a wealth of contextual information that greatly benefits AI models. In addition, many recent video annotation services and tools are equipped with additional features that improve work efficiency with large video data sets and make it easier for data labeling teams to work with.
Annotating video footage has many benefits, especially for improving the training of AI models and expanding real-world applications. Here are some key benefits:
Efficient annotation with interpolation
Some AI annotation tools simplify the process by allowing interpolation. Instead of annotating each video frame yourself, you can simply mark the start and end of a sequence. The software then automatically fills in the intermediate images, saving time and effort.
Capturing temporal dynamics
Videos, unlike static images, encapsulate movement and change over time. By annotating videos, you're providing rich data that helps AI models understand object movements and temporal variations—something that static images can't convey.
Richer data for AI training
Videos naturally contain more information than single images. This abundance of data, once annotated, provides a comprehensive learning resource for AI systems. Such detailed data entry can greatly improve the accuracy and performance of AI models.
Profitability and time efficiency
Video annotation can be more economical compared to image annotation. A single video can provide numerous data points, and focusing on key frames reduces the overall time spent on the annotation process.
Broader real-world applications
Annotated videos provide a more accurate representation of real world scenarios. This increased realism is crucial in training AI models for diverse applications, ranging from sports analysis and medical diagnostics to agricultural surveillance.
📣 It is important to recognize that Video annotation is always a complex and time-consuming task. Effective use of video annotation requires the mastery of tools and workflows specific. Despite these challenges, the benefits of video annotation in advancing AI capabilities and applications are undeniable.
What is the role of data annotators (or video annotators)?
The role of a video annotator is essential in the development of sophisticated artificial intelligence systems.. By meticulously adding labels and tags to various segments and video footage, these professionals annotate videos to provide the critical data needed to train AI models. This process, commonly referred to as annotation, is essential for teaching AI systems to accurately identify objects, and to understand various objects, actions, or patterns in video content.
Beyond simple recognition, annotated videos can also teach AI the context, movement, and interaction of different elements within a scene. This is especially important in areas such as the development of autonomous vehicles, where understanding dynamic environments is crucial, or in healthcare, where AI helps to interpret complex medical images.
In addition, the quality and accuracy of video annotation directly impact the effectiveness of AI training. Data annotators need to provide accurate video annotations to ensure that AI models learn properly, reducing errors in real applications. This requires them to have a keen eye for detail and a good understanding of the subject. As with any other job, it requires training and experience.
In addition, video annotation is evolving with the advent of new technologies. Advanced tools, such as video annotation software automated systems and their specialized machine learning algorithms for annotation, are increasingly being used to streamline the process, making it faster and more efficient. However, human supervision remains essential to ensure the reliability and accuracy of the data provided to AI systems.
🔖 Video annotators play a major role in Computer Vision algorithms by bridging the gap between raw video footage and intelligent AI applications, allowing for the creation of smarter and more intuitive technology solutions across a variety of industries.
The video annotation process for Computer Vision models
Now let's take a closer look at the video annotation process that is fundamental to the development of Computer Vision technologies. A streamlined AI annotation pipeline can help solve the video annotation task effectively. The following methodology can be applied for the successful and rapid delivery of tagged video data:
Selection and training of annotators
The process starts with assembling a team of qualified annotators. They should receive comprehensive training to align with the goals of the project. Their expertise, especially in understanding the nuances of the project, is essential. Integrating their skills with AI-assisted tools (for example, a recent and collaborative data annotation platform) can greatly speed up the manual annotation process while ensuring accuracy.
Selecting tools for improved annotation
Choosing the right video annotation tools is crucial. Select tools that offer advanced video management capabilities, a user-friendly interface, and features such as event-based classification, dynamic object tracking, and efficient interpolation. The tool should also support effective project and team management, ensuring a smooth workflow.
Defining project goals and preparing data
Setting clear project goals early on establishes a focused direction for the annotation process. Depending on the goals, such as differentiating between animal species or categorizing objects by color, the type and volume of data required will vary. Collecting a diverse data set that covers various scenarios and objects is crucial for developing a robust AI model.
Implementing annotation strategies
With the goals and tools in place, develop a consistent annotation strategy. This includes creating guidelines that ensure that all annotated data is consistent and annotating videos correctly. It is important to adapt these strategies to the needs of the project, aligning them with the expected results of the AI model.
Iterative review and refinement
Continuously reviewing and refining annotations is key. Regular quality checks and updates not only improve annotations but also correct inaccuracies. Edit annotations based on feedback on the performance of the AI model, ensuring continuous improvement and relevance.
Feedback loop for continuous improvement
Establish a feedback loop that uses information from the performance of the AI model to refine both the annotation process and the training of annotators. This ensures that the project evolves alongside the model, leading to more accurate and effective annotations over time.
ℹ️ By adopting this methodology, the video annotation process can be made more efficient, facilitating the development of advanced AI models in Computer Vision.
Some video annotation techniques
Below, we describe several video annotation methods and best practices that can contribute to the success of your projects. This list, although not exhaustive, can be communicated to annotators as part of their training:
Understand the task and choose the right annotation method
It is important to first understand the specific requirements of the data set and the task, and especially the business. Just as Data Scientists cannot work in silos, Data Labelers need to have a solid foundation and ideally industry knowledge of your use case. For example, specialized Data Labelers have expertise in medicine, and are therefore in a position to annotate medical videos.
An annotation strategy is also necessary for any project, and must take into account the preferred techniques and methods, to be communicated to the team of annotators, and to be configured in the labeling platform. For example, estimating human posture in video data requires the key points method to label human joints. On the other hand, an annotation per enclosing box alone may not provide enough detailed information. The choice of annotation method should correspond to the objectives of the project to ensure In fine effective training of the AI model.
Unique image annotation method
This traditional approach involves breaking the video down into individual frames or frames, which are then labeled separately. Suitable for videos with minimal object movement or smaller data sets, this method can become time consuming and expensive for larger video data sets due to the huge number of frames that require annotation.
Multiframe or stream annotation method
In this method, objects are tagged in video streams. Annotators track objects and their coordinates frame by frame as the video unfolds. Faster and more efficient, this approach is better for larger data sets, ensuring accuracy and consistency in object detection and labeling, and gaining popularity with advances in video annotation tools.
Tools for automatic annotation of continuous frames
Recent advances in video annotation include automatic annotation features that simplify the process. Machine learning algorithms track objects across frames, maintaining continuity. These algorithms analyze pixel movements between frames to accurately track objects that may appear intermittently throughout the video. Automation reduces annotation workload and improves data consistency.
Adapting to evolving annotation needs
As AI and machine learning technologies advance, video annotation methods need to evolve as well. Annotators and project managers need to stay up to date with the latest tools and techniques to ensure that video annotation services and process remain effective, accurate, and up to date with AI developments and various Computer Vision models and techniques.
What about video annotation tools and platforms?
Video annotation platforms come in paid and free versions, each serving different purposes and user groups.
What are the benefits of a paid video annotation tool?
Paid annotation platforms are generally preferred by professional teams involved in commercial Computer Vision projects, especially those in the areas of machine learning. These platforms are designed to manage large data sets and provide comprehensive support throughout the machine learning lifecycle, through advanced features such as collaboration or machine annotation. Key features of these paid platforms often include:
- Sophisticated ontology capabilities for complex sub-classifications within labels.
- The ability to manage and annotate long videos and image sequences.
- Extensive features for various types of annotation, such as bounding boxes, polygons, polylines, key points, and primitives.
- Of workflows customizable for review and annotation, allowing for effective monitoring of annotator performance and task allocation.
- Advanced features that allow automated pre-annotation, taking advantage of proprietary algorithms or AI models to streamline the process.
- Collaborative functionalities allowing annotation by consensus, as well as precise management of manual annotation activities.
Free video annotations? What is the trick?
Conversely, free video annotation tools seem to be a preferred choice for academics, AI enthusiasts, and students. These tools are well suited for individuals or groups working on localized projects or those who do not need scalability for their projects. The free tools offer essential video annotation services and capabilities, making them ideal for educational purposes, for small-scale research, or for personal machine learning projects.
However, is it impossible to carry out large-scale projects with these tools? It is clear that recent advances prove the opposite. Open source projects like Label Studio have demonstrated that they have powerful embedded functionalities, capable of supporting ambitious projects. This tool offers remarkable flexibility in terms of the types of annotations supported and customization, allowing users to adapt it to varied and complex projects. Its Open Source nature promotes continuous improvement thanks to an active community and a constantly evolving range of functionalities. This demonstrates that, while designed to be accessible and inexpensive, some free video annotation tools can rival paid options in terms of capabilities and efficiency, making high-quality video annotation more accessible to a wider range of users and projects. Not convinced? Take the time to watch the following video:
📝 In summary - each type of platform brings its own set of benefits. While paid platforms offer extensive features and a scalability For larger, complex projects, free tools provide an accessible and cost-effective option for learning, experimenting, and smaller scale tasks.
5 challenges and pitfalls in video annotation
1. First pitfall: underestimating the complexity of annotation
The art of detail in video annotation
One of the biggest mistakes AI development teams make is underestimating the complexity inherent in video annotation. Often thought of as a simple tagging task, video annotation is actually much more nuanced than it seems.
Videos can contain a multitude of objects, actions, and contextual information that require close observation and highly accurate annotations. Ignoring this complexity can result in inaccurate annotations, misleading AI models during training. To overcome this challenge, project managers need to spend time defining clear annotation guidelines, properly training annotators, and establishing a quality review and correction loop, to ensure consistency and quality throughout the video annotation process itself.
2. Lack of communication with annotators
Foster collaboration for successful annotation
Seamless communication between project managers and annotators is of paramount importance for accurate video annotations. A lack of communication can lead to confusion about tagging criteria (sometimes ambiguous), project goals, and deadlines.
To overcome this problem, open communication is essential. Regular meetings, daily follow-up via information-sharing platforms, clear documentation of guidelines, and quick answers to annotators' questions can greatly improve the quality of annotations. Building a sense of collaboration and shared responsibility ensures that the entire video annotation services team is aligned with project goals.
3. Overlooking the iterative nature of the data annotation process
Video annotation: an evolving process
Video annotation is rarely a one-time project. Annotating a large volume of video data involves iterative cycles of first rapid annotation, then review and refinement. Some organizations fall into the trap of viewing annotation as a linear task with a fixed end. However, embracing the iterative nature of annotation allows for continuous improvement in the accuracy and quality of annotation.
Regularly reviewing annotated video data and incorporating feedback on the performance of the AI model, making adjustments as needed in response to the emergence of new patterns, are essential for refining the annotation process and achieving better results over time and testing.
4. Underestimating the importance of quality control
Ensuring the consistency of labelled data through quality control measures
Quality control is the cornerstone of any successful video annotation project. Without effective quality controls, inconsistencies and errors can creep into the annotated video dataset itself, negatively affecting the performance of the AI model. Setting up a robust quality control mechanism (with Labeling Managers in charge of iterative quality reviews, for example) involves random checks, double annotations for verification, and periodic audits to identify and correct inaccuracies.
A complementary approach can also be to set up “consensus” reviews, by asking several Data Labelers to review the same set of video data, in order to select the results that meet with consensus to guide the model. By implementing rigorous quality control measures, AI teams can ensure the reliability and accuracy of their self-annotated video datasets, which very often results in better AI models.
5. Errors associated with the performance problems of labeling platforms, in video annotation projects
An important but sometimes overlooked element in video annotation projects is the performance of the labeling platforms used. The quality of the annotations depends largely on the tagging tool used by the annotators. Robust platforms offer features to effectively manage the complexity of video annotation tasks, such as accurate time synchronization, functionality to pre-annotate or draw labels easily and accurately, and advanced project management capabilities.
Additionally, when it comes to low-quality or low-resolution videos, high-performance labeling platforms are all the more essential. Issues such as blur, noise, or compression can make annotation difficult and cause errors. Labeling platforms with enhanced visualization tools and magnification capabilities can help annotators accurately identify objects and label objects and actions, even under challenging conditions.
In conclusion...
Video annotation projects offer considerable potential for training AI models to accurately recognize and interpret visual content. By learning from the mistakes and the main pitfalls discussed in this article, it is possible to build an effective video labeling process.
Recognizing the complex nature of video annotation tasks, fostering open communication, embracing the iterative process, and prioritizing quality control are the pillars upon which successful video annotation projects are built.
To find out more about video annotation projects with Innovatiana... In this article, find out how Innovatiana Data Labelers have a real impact on complex video annotation projects, especially those involving delicate medical videos (link). Dive into this article describing one of our successful collaborations with SDSC. And if you want to know more about our services, do not hesitate to ask us for a quote.
