Video Labelling (or Video annotation for machine learning) is a means of labeling information to improve its usability in training machine learning (ML) algorithms. With video annotation, metadata is added to video datasets. This information can include specifics on people, locations, objects, and more.
Artificial intelligence recognizes patterns in text copy, images and videos. When for example more and more videos are being uploaded to online portals, the need for efficient monitoring and classification grows. Today the labeling of videos is mostly automated. Precisely because video data is more complex than copy and unmoving images, the demands on machine learning are correspondingly greater.
There are basically two different strategies for teaching a program the classification or annotation of video data:
High-quality AI training data for machine learning fulfills all requirements for a specific learning objective. The quality of the results reflects the quality of the training data, specifically the performance of trained AI algorithms.
The benefit of automatic video recognition is evident. Artificial intelligence – trained with annotated videos / labeled videos – optimizes video monitoring. In this way, for example, a fire, panic breaking out in a mass of people, or unusual vehicle movement can be recognized in seconds. But machine learning is also useful for labeling more nuanced video features like sentiment.
While video annotation is useful for detecting and recognizing objects, its primary purpose is to create training data sets. When it comes to video annotation, there are several different steps that apply.
For video annotation, several tools stand out for their functionality, ease of use, and community support. Here are some of the best open-source video annotation tools:
Developed by Intel, CVAT is a MIT licensed, robust web-based tool for data labeling tasks including annotating video and image data. It supports multiple annotation formats, including boxes, polygons, polylines, and points, which are essential for tasks such as box annotation and point annotation.
CVAT also offers semi-automatic annotation capabilities and integration with pre-trained models for auto-labeling, enhancing the quality of the training data. This tool is particularly useful for managing large datasets and ensuring high data quality. It has a Python SDK for easy integration into workflows for your video annotation projects.
Although primarily known for image annotation, VIA also supports video annotation tasks. It offers a versatile and user-friendly interface for annotating video clips frame by frame, supporting various shapes such as points, polygons, rectangles, and ellipses. VIA is open-source and serves as collaboration tool among multiple annotators, enabling them to export annotations in multiple formats. This makes it an excellent choice for projects requiring detailed segmentation.
This tool supports both image and video annotation and offers a wide range of annotation tools such as bounding boxes, polygons, and semantic segmentation. Supervise.ly also provides AI-assisted labeling and project management features, making it suitable for team collaboration on large-scale projects that involve complex annotation tasks. The tool’s ability to handle various annotation features makes it highly versatile.
Although more geared towards image annotation, Annotorious is a JavaScript front end library that can also be used for video annotation tasks. It offers a simple and user-friendly interface, supports various annotation types, and allows for real-time collaboration among annotators. However, it may be less suitable for large-scale or complex video annotation tasks due to its simplicity compared to other tools like CVAT which offer more advanced features like semi-automatic annotation and integration with pre-trained models.
These tools are highly regarded for their features, community support, and the ability to integrate them into various workflows, making them some of the best open-source video annotation tools available at the time of writing. They are particularly useful in enhancing the efficiency of video annotating processes and ensuring high-quality outputs through advanced algorithm integration and robust data quality checks.
To semi-automate annotation using a combination of human-in-the-loop (HITL), open-source annotation tools, and multimodal Large Language Models (LLMs), you can follow a structured approach that leverages the strengths of each component. Here’s a step-by-step guide on how to implement this:
Humans are essential for providing nuanced judgment, contextual understanding, and handling edge cases that automated systems may struggle with. They should be involved in the annotation process to ensure accuracy and consistency.
Use humans to review and correct automated annotations. This feedback loop is crucial for improving the model’s performance over time. Humans can annotate a subset of the data, and then the model can learn from this annotated data to automate the annotation of the rest.
Utilize open-source tools like CVAT (Computer Vision Annotation Tool) or the others mentioned above.
Automation Integration
– Integrate these tools with scripts or APIs that can automate parts of the annotation process. For example, CVAT supports semi-automatic annotation and can be integrated with pre-trained models for auto-labeling.
Pre-Annotation with Multimodal LLMs
Use multimodal LLMs such as GPT4o, Pixtral or LLaVa to pre-annotate the data. These models can generate initial annotations based on the keyframes within the input data, which can then be reviewed and corrected by humans. This step significantly reduces the manual effort required for annotation.
Active Learning
Implement active learning strategies where the LLM identifies the most uncertain or challenging samples and requests human annotation for those specific cases. This approach ensures that human effort is focused on the most critical parts of the dataset.
1. Data Preparation:
– Use open-source annotation tools like CVAT or VIA to prepare the initial dataset.
Pre-annotate the data using multimodal LLMs to generate initial labels.
2. Human Review and Correction:
– Have humans review the pre-annotated data and correct any inaccuracies.
Implement a feedback loop where human corrections are used to update the LLM, improving its accuracy over time.
3. Active Learning:
Use the LLM to identify samples that are most uncertain or challenging and request human annotation for those cases.
This ensures that human effort is targeted and efficient.
4. Automation and Iteration:
Automate the annotation process for less complex samples using the updated LLM.
Continuously iterate between human review, LLM updates, and automated annotation to refine the model and improve its performance.
5. Quality Control and Consistency:
Ensure consistency in annotations by using clear guidelines and training for human annotators.
Use tools like Labelbox or other open-source annotation platforms to manage and monitor the quality of annotations.
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