Video annotation, the process of labeling and marking objects or actions within video sequences, is crucial for building robust machine-learning applications such as object detection, action recognition, and autonomous vehicles.
By empowering computer vision models to recognize objects, movements, and actions within visual content, video annotation has far-reaching applications across industries. These video annotations can range from simplistic object identification to identifying complex actions and emotions. Here are some examples:
- Video annotation can help train AI models to detect objects in video footage, such as cars, road damage, or animals.
- AI can track objects in video footage and predict their future locations with video labeling, making it valuable for tasks like monitoring pedestrians or vehicles for security.
- AI models can locate objects in video footage and provide their coordinates for monitoring occupied and unoccupied parking spaces or coordinating air traffic.
- By categorizing different objects through annotations, AI models can create complex classification systems. For example, a system could use video footage to group and count ripe and unripe berries.
If you’re new to video annotation and wondering how to get started, this step-by-step guide will walk you through the process, from understanding the annotation task to integrating annotated video data into machine learning frameworks.
Step 1: Understand the Annotation Task
Video annotation can involve various types of annotations, including bounding boxes, key points, and segmentation. Define what you need to label within the videos, whether it’s identifying objects, tracking movements, or recognizing actions.
Step 2: Choose the Right Tools
Factors such as the project’s complexity, budget, and team size are critical considerations when choosing. The solution you select should align with your specific annotation needs, should be scalable, and should support automation.
Step 3: Prepare Your Video Data
Ensure your video data is in a suitable format, resolution, and quality. If necessary, perform preprocessing tasks like resizing, frame extraction, and de-noising to improve the video quality.
Step 4: Set Up the Annotation Environment
Once you’ve chosen an annotation tool, follow the instructions for installation and configuration. Most video annotation tools provide documentation and tutorials to assist you in the setup process.
Step 5: Create Annotations
Use the annotation tool of your choice to label objects, define attributes, and annotate over video frames. Refer to your task definition and guidelines to ensure accuracy and consistency. It’s also essential to consider factors such as scale, orientation, and occlusion when annotating objects in video frames.
Step 6: Review and Quality Control
Before you consider your annotations complete, undertake a rigorous review and quality control process. Identify errors, inconsistencies, or missing labels.
Step 7: Export Annotations
Once you’re satisfied with your annotations, export the annotated data in a format suitable for your machine-learning framework. Common formats include JSON, XML, or CSV.
Step 8: Integration with Machine Learning
Libraries and tools in Python can integrate data into your machine-learning framework. You can use code to load and work with the annotated data in your machine-learning projects.
Why Video Annotation for Machine Learning
Annotating videos offers several advantages over annotating individual images:
Interpolation
AI annotation tools can automate the process, allowing for annotations only at the beginning and end of a sequence, with the in-between annotations generated automatically.
Context
Videos contain motion, which can be challenging for static image-based AI models to learn. By annotating videos, the AI system gains information about object movement and changes over time.
Real-World Applications
Annotated videos represent real-world situations better, enabling advanced AI models across various fields, from sports to medicine and agriculture.
However, video annotation remains a complex and time-consuming task. Video annotators must learn the appropriate tools and workflows to navigate this process efficiently. When working with video annotation, factors such as data quality, organization, handling overlapping objects, utilizing interpolation and keyframes, and leveraging auto-annotation need focus to save time
iMerit Video Annotation Tool on Ango Hub Platform
The iMerit Video Annotation tool, integrated with our Ango Hub platform, significantly improves the efficiency of video annotation tasks and substantially reduces the time required for annotating videos, even when dealing with a large number of categories. Our user-friendly interface offers a comprehensive view of annotations, streamlining the review process for annotation tasks to enable reviewers to swiftly assess and confirm details directly from the timeline.
- This solution supports a variety of formats, including .mp4, .mov, .webm, .ogg, and multi-frame DICOM .dcm files.
- You can work with videos of up to 1 hour in duration at a time, with resolutions up to 2k.
- No matter how many labels you need, our tool accommodates them all, including Bounding Box, Rotated Bounding Box, Polygon, Polyline, Segmentation, Point, and Brush annotations.
- The timeline view feature helps visualize the annotations and easily add or delete keyframes within our solution.
- You can utilize frame interpolation, enabling you to draw polygons or segmentations in one frame and then apply them across multiple frames in the video.
- Real-time project monitoring provides valuable insights into the performance of each annotator, including metrics such as the number of labels, Time per Task (TPT), and accuracy.
Conclusion
Our solution is highly scalable and capable of processing both a few hours of video and several terabytes of video data with equal efficiency. With a team of expert annotators and cutting-edge technology, we ensure that annotated data is of the highest quality, making it a valuable resource for training and enhancing machine learning models.
