We’re here to make you understand what is video annotation, and how to annotate videos better.

Video annotation is the process of labeling video objects or images frame by frame. The main intention is to have data that can be used in AI/ML-based systems.

When we talk of video annotation, there are different ways you can effectively annotate videos. You can use the 3D bounding boxes to annotate cars and human beings on a highway, or key point to annotate players in a field.

What Is Video Annotation?

Think of video as still images played in a sequence. In a video context, these still images are referred to as frames. In annotation, the frames are given a name such as a cat, hard hat, car, shark, etc. 

Standard videos have 24 frames per second; a fact that makes annotating videos more complex compared to annotating images. 

Video annotation involves labeling or tagging video frames in a video clip for the purposes of machine learning. This process is usually done by human annotators with the aid of video annotation tools. The key purpose of video annotation is to make it easier for machine vision models to detect, and recognize objects.

In video annotation, the annotator plays a video clip, pauses then scrutinizes, and labels the still images one by one.  The labeling process can be manual or automated. Companies that require massive databases of training data rely on video annotation tools to automate the annotation and labeling process.

The labels or tags on each single video frame plus the still image are the two main video elements used in computer vision training. 

Video Annotation Use Case Examples

Experts use data derived from video annotation to train artificial intelligence and machine learning models. The ML models are used in nearly every retail and manufacturing segment. Whenever data analysis for marketing purposes or detection of defects in manufactured goods is to be carried out by AI and ML models, video annotation has to be applicable.

Here are the uses of video annotation.

1. Retail

In retail, video annotation is used in monitoring and tracking shoppers’ movements as well as the flow of products. Monitoring shoppers help prevent shoplifting by alerting security if items aren’t scanned. For AI retail systems to be trained, the models have to be trained using video annotation.

2. Healthcare

Robotic surgery is today adopted in many hospitals due to low surgical risks and faster healing. Surgical videos are annotated to construct databases for training robotic surgery AI systems.

The Apollo Institute of Robotic Surgery in India is renowned for robotic surgery of the prostate, and kidney, and in specialties like Gynaecology, and Cardiac.

3. Automotive

Video annotation is widely used within the automotive sector. For self-driving vehicles to recognize lane marking, and incoming objects and stick to their lanes, their AI systems need video training data. 

Equally, automatic collision braking systems and emergency braking systems in vehicles like Tesla and Volvo depend on data derived from video annotation.

The Best Video Annotation Service To Choose

The increasing demand for data annotation services is attributed to the rapid penetration of robotics in the retail, healthcare, and manufacturing sectors. The incorporation of AI in automotive is another factor. 

With global data collection and labeling market size is projected to grow at a CAGR of 24.9% to reach $15.5 billion by 2030. That is down from $2.1 billion in 2021.

If you work with artificial intelligence-based systems, the accuracy of your database determines the system’s success rate. For consistent accuracy, frequently feeding new and fresh data such as annotated videos and images to the AI system is essential. 

Why You Need Wordpath Video Annotation Services

• Video annotation outsourcing from renowned and trusted big data solutions companies like Wordspath lifts up the burden of data collection and labeling.
  
• With over 30,000 verified annotators spread across the globe, Wordspath has the potential to keep your databases refreshed with reliable language-specific annotated video data.
  
• To train your machine learning models, unstructured raw data has to be categorized and labeled for quick identification. Being ISO 17100:2015 and ISO 9001:2015 certified, you can depend on Wordspath video annotation services for seamless data collection, extraction, and annotation.
  
• Besides video annotation services, Wordspath offers image annotation services, text data annotation, and sound labeling for conversational AI models.

7 Methods for Video Annotation 

In selecting the right technique to annotate your videos, you can opt to use the single frame annotation technique or the continuous frame video annotation. 

In single frame annotation, annotation of video frames is done frame by frame. With a one-minute video carrying an average of 1700 frames, this technique is open to risks, costly, and takes longer compared to the continuous frame method. 

Continuous frame video annotation is the most used technique and it involves annotating the videos as a stream of frames. Its main advantage is it maintains the continuity of flow of the information.

There are many different methods used when annotating videos. The type of video annotation an annotator uses is determined by the kind of data he/she is collecting, the nature or structure of the frame, the degree of accuracy desired, and the intended use of the data.

Let’s take a closer look at the common methods for video annotation.

1. 2D Bounding Boxes

Labeling video frames with bounding boxes is the easiest annotation method. This method entails drawing a rectangle box around an object or image in a video and tagging it. The rectangular box has to touch the edges of the object being labeled. 

A practical example of a bounding box method is training AI models on cars in traffic. Here, you’ll need to first have a video on the same. The next step is to annotate the boxes by drawing a rectangular box precisely touching the edges.

Too many overlapping bounding boxes and leaving gaps at the object edges undermine the accuracy of object detection. 

2. Ellipse Annotation    

As the name suggests, in eclipse annotation you draw circles on circular objects in a video. Drawing bounding boxes on circular video frames leaves large unoccupied background. It also increases the probability of other images appearing in the background.    

3. 3D Cuboid Annotation

Robots used in warehouses are sometimes trained on 2D images annotated using the 3D cuboid annotation method. Again, this method is relied on when annotating cuboids such as a book, wooden boxes, and mobile phones. 

In definition, 3D cuboid annotation is the labeling of objects in 2D shapes with a cuboid. This annotation type is used when the object’s depth is critical for machine vision to recognize it.    

4. Polygon Annotation

The Polygon annotation method is used on objects with an irregular form whose vertices need to be connected. All the x, and y axis along the edges have to be connected. Its use cases can be found in the geospatial, medical, industrial, agricultural, and automotive sectors. 

5. Landmark Annotation

Landmark annotation also called dot annotation is used to generate points of correspondence on an object or image such as a human face. It’s mostly used in vision tasks where the face, facial gestures, gaze direction, and emotion detection are required. Its use case can be found in sports, unlocking cell phones, and maps.  

6. Semantic Segmentation Annotation

                                                   Image courtesy- keymakr

In some AI system models, objects are best identified after analyzing all the pixels in an object or image. Pixel is the smallest unit in an image. An image is composed of millions of pixels.

Semantic segmentation annotation is the labeling of objects or images with every pixel composing it. This method’s application is found in autonomous vehicles. 

7. Key Point Annotation Method 

Key point annotation is widely used in sports to track body parts movements for sports analytics and facial emotion recognition. This method is more detailed on objects as the key points are connected no matter how close they are.

Key point annotation entails labeling an object’s landmarks on every edge for motion detection, recognition, and tracking. 

How to Annotate a Video: Step by Step 

If you can annotate an image, you can also annotate a video. Of importance when planning to annotate a video is to understand how and where the annotated video will be used. Knowing the objects or images contained in the video will help determine the method of annotation to use.

Whereas manual annotation is cheaper compared to video annotation outsourcing from an agency or company, the process is slow, tedious, and full of errors. Alternatively, you can auto-annotate video data. Data annotation tools like Dataloop AI, Markup.io, and VEED are recommendable for auto-annotation on videos. 

Read further, and we will show you how to annotate a video step by step using the bounding box method, but other methods are kind of the same.

Step 1. Uploading video footage to the video annotation tool

After selecting a video annotation platform of your choice, select the video intended for annotation from your device. Next, upload the video.

Step 2. Setting your desired frame rate

While considering the number of video frames per second and the application of the annotated data, set the appropriate frame rate. Average videos have between 25-30 frames. If the data is intended for tracking purposes, it’s advisable to set low frames per second.

Step 3. Creating video annotation labels

Having reviewed the video footage, you already know the category of objects composing the video. If the categories are varied, include all the categories in the boxes given as your video annotation labels. 

Step 4. Choosing the appropriate annotation method

                                                           Image courtesy- keymakr

Are the objects in the video best annotated using the bounding box method? Whichever data annotation platform you use, you’ll have annotation method options to select the most suitable for video markup.

For AI/ML-based systems to accurately interpret annotated objects and images, choose an annotation method based on the annotated video’s intended use. 

Step 5. Auto-annotating objects

With the annotation labels set and method selected, annotate the objects precisely as it determines how reliable and accurate your AI model will be. 

Remember, if the bounding box doesn’t cover the object’s background, choose a different method.

Step 6. Manually check for correctness and add missing target objects 

No one is 100% perfect at all times when handling data. Incorrect video annotation and failure to annotate some video images is common whichever method you use. To ensure completeness and accuracy in any kind of data, you should manually recheck your annotated video for correctness and then add any missing target objects.

Common Used Video Annotation Tools 

The three categories of video annotation tools available are online video annotation tools, open-source annotation tools, and free video annotation tools. While some tools will cost you to use, others are free. In determining the right tool to use, consider the size of the video to annotate, the complexity of the video, and the video annotation labels or classes needed.

Ease of use, ability to integrate with other tools, capability to do multiple annotation types, and shareability are other important features to factor in when selecting the best video annotation tool.

Here’s a list of video annotation tools you can use.

1. Labelbox

With Labelbox, you use the bounding boxes, Semantic Segmentation Annotation, or Polygon Annotation method. Since most video frames are usually repetitive, Labelbox helps save time by automatically adding labels on matching data. To add more aiding labels to the annotated video data, you can attach related images, overlays, texts, and videos. 

2. Flixier

With Flixier, you’ll not have to download, register to use, pay or subscribe to use. It’s an online video annotation tool that allows users to draw on their video, add animated texts, labels, and titles, annotate images with bounding boxes, and other methods.

To use Flixier, upload your video from your saved files or directly from YouTube by copy-pasting the video URL. Next, annotate the video, download it, and save it.

3. Deepen.ai

To train some AI-based models, you’ll need to annotate videos using different methods. With Deepen.ai, it’s easy to use segmentation, 3D cuboids, and bounding boxes simultaneously to auto-annotate your videos. This annotation tool has a friendly user interface. 

They also pride themselves in annotating videos rich in labels and tags for better object tracking and monitoring for AI-based computer vision models.

4. Cvat.ai

To annotate videos faster with your own AI model, CVAT is the tool to integrate with. It’s perfect for data annotation intended for detection, tracking, and classification. Most users of this tool are found in healthcare, automotive, retail and manufacturing, sports, and athletics.

The 3D Cuboid, Skeleton, Point Clouds, and Instance Segmentation video annotation features make CVAT a great tool for video annotation in all sectors.

5. SuperAnnotate

SuperAnnotate is video annotation software used by AI enthusiasts to annotate videos for accurate object detection, classification, tracking, and training. Other data annotation services this tool offers include audio, text, LiDAR, and custom data annotation.

You can use this tool to create an automated workflow as it’s rich in collaborative features.  

Video Annotation Best Practices

To have reliable video datasets that will make AI models make accurate decisions based on your data, adhering to video annotation best practices is inevitable. Maintaining the quality of the video objects and being consistent throughout the annotation are key factors video annotators should observe.

Choosing the right annotation technique, method, suitable video frame rate, and correct labeling are among the best video annotation practices.

Here are the practices that would lead to effective and efficient video annotation.

1. Using correct labeling and tags

ML models can be rendered useless if trained with incorrect information. When creating labeling classes and tags, giving each video object its right label or tag produces quality datasets crucial for training ML models. Structuring the labels and assigning custom metadata to objects results in their correct classification.

2. Interpolation of objects

Objects in video footage whose shape and keyframes remain constant throughout should be annotated and labeled at a point where all critical data is sufficient. To maintain the object’s quality, interpolate and annotate the whole video. Interpolating speeds up the annotation process and maintains the quality.

3. Maintaining accuracy and precision

Drawing 3D cuboids, bounding boxes, or even using auto-video annotating software to draw dots seems easy. But without accurately drawing these boxes and dots, the video data will lead to training ML models with incorrect data. Accuracy and precision should at all times be maintained.

Conclusion 

Video annotation is the process of labeling and tagging video objects frame by frame using either single frame or stream frame technique. Different methods are used to annotate video; bounding boxes, eclipse, skeleton, 3D cuboid, semantic and polygon are the common methods.

With the availability of video annotation software both free and paid, you don’t require special training to know how to markup a video. Just select a video annotation tool with a user-friendly interface, shareability, and integration features and ensure it supports all annotation methods and you’ll not need any help in annotating videos.

But for you to concentrate on training your AI/ ML models or working on AI-based systems, you can request video annotation services from trusted big data specialists.