Video Annotation for Machine Learning / Video Labeling

Video Annotation / Video Labeling for AI Algorithms

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:

• For the monitored classification of incoming data, videos are tagged in advance. For example, a video depicts a moving car or not. This information is provided to the program together with the data in ongoing training.
• Unmonitored classification trains computer programs in video annotation / video labeling by using segmentation or clustering algorithms. The program recognizes differences and similarities in a multitude of data samples.

Creation and Annotation / Labeling of AI Training Data Sets

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.

• With video annotation / video labeling, for example, crowd workers work on a large number of videos based on concrete guidelines and label / annotate these videos for AI algorithm training purposes.
• Conversely crowd workers all over the world label / annotate existing videos, so that they can be used as datasets for monitored or reinforced learning.

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.

Video annotation for machine learning in the World of AI

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.

1. Frame-by-frame detection – With frame-by-frame detection, individual items of interest are highlighted and categorized. By capturing specific objects, detection with ML algorithms can be improved.
2. Object localization – object localization helps to identify specific images within a defined boundary. This helps algorithms find and locate the primary object in an image.
3. Object tracking – often used with autonomous vehicles, object tracking helps detect street lights, signage, pedestrians, and more to improve road safety.
4. Individual tracking – similar to object tracking, individual trackings is focused on humans and how they move. Video annotation at sporting facilities help ML algorithms understand human movement in different situations.

Video Annotation Tools

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:

CVAT (Computer Vision Annotation Tool)

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.

VGG Image Annotator (VIA)

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.

Supervise.ly

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.

Annotorious

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.

How to annotate videos with auto-annotations

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:

Human-in-the-Loop (HITL) Integration

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.

Workflow

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.

Open-Source Annotation Tools

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.

Step-by-Step Process

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.

Video Annotation Services

Enhance your AI models with our comprehensive video annotation services. Our skilled clickworkers meticulously label and annotate video content, providing high-quality training data for computer vision and video recognition systems.

Our video annotation services include:

• Object tracking and labeling
• Action and event tagging
• Semantic segmentation
• Bounding box creation
• Keypoint annotation for human pose estimation

Whether you’re developing surveillance systems, autonomous vehicles, or gesture recognition software, our precise video annotations will help improve your ML projects’ accuracy and performance.

Ready to take your machine learning models to the next level? Learn more about our video annotation services and how they can accelerate your AI development.