AI models that realize video analysis, unlike image analysis, enable recognition unique to video, allowing for AI utilization in a wider range of areas. For example, it becomes possible to predict the movements of people and vehicles in autonomous driving, or to analyze the movements of individuals through surveillance cameras.
AI models capable of video analysis are actually utilized in many fields such as retail, government, critical infrastructure, transportation, and medicine.
However, the use of video data obtained through cameras and other devices requires an important preprocessing step: video annotation. Video annotation is basically an extension of image annotation and requires considerable effort and time. So, how can this process be executed efficiently and cost-effectively?
In this article, we start with the basic definition of annotation, clarify its types and procedures, and explain in detail how using appropriate tools can reduce the cost of the annotation process. This is a very valuable article for business owners considering outsourcing video annotation.
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Video annotation refers to labeling objects within a video.
For example, in traffic surveillance camera footage, it means identifying vehicles, pedestrians, bicycles, etc., and labeling each of them. By using this labeled video data, AI models can learn the movements and patterns within the video.
Video annotation is used for training models that recognize moving objects and plays an important role in surveillance camera and autonomous vehicle sensor technology. For example, it allows for the detection of suspicious movements from surveillance camera footage or provides information for autonomous vehicles to understand the surrounding environment and operate safely.
Due to the advancement and lowering costs of video annotation technology, AI models can now efficiently perform monitoring and safety confirmation tasks that were previously done by humans.
Video annotation is considered an extension of image annotation. The difference between video annotation and image annotation is that video annotation captures information that changes over time.
Video annotation is basically a series of image annotations and involves labeling objects in each frame.
Furthermore, it tracks the movement of objects through consecutive image frames. In terms of learning the patterns of movement of target objects, it requires more effort than image annotation.
However, the consecutive data obtained through video annotation helps in understanding the movement and direction of objects, and predicting these patterns is extremely valuable.
The difference between the single image method and the continuous frame method greatly affects the efficiency and precision of annotation work. While the single image method performs annotation individually on each frame, the continuous frame method automatically generates annotations for intermediate frames based on the annotations of the start and end frames.
The single image method is a technique for performing annotation on every frame (image) that constitutes a video. The number of frames per second in a video is expressed in fps (frames per second). If it is 5fps, it means 5 images are used per second.
Since the frame rate of a general camera is about 15 to 60 fps, to annotate a one-minute video using the single image method, you must annotate 900 to 3,600 images. While this requires a very large amount of effort, it allows for accurate annotation of each frame.
On the other hand, with the continuous frame method, when you label the first and last frames, the frames between them are also automatically labeled. While it can save time and effort, it may be difficult to handle rapid movements or deformations of objects.
Consider which method to adopt by categorizing your company's case as follows.
- High precision is required
- Resources and time are abundant
- Complexity of object movement: There are complex movements or sudden changes
- Efficiency is prioritized
- Desire to proceed with work efficiently within limited resources and time
- Simple movements
AI models for video recognition built through video annotation can perform the following:
| ① Task Automation ② Prediction of the Next Action ③ Real-time Processing ④ Recognition of Objects Only Partially Visible |
AI models can learn from video annotation data to automatically perform tasks that were previously done by humans. Especially in areas such as anomaly detection and autonomous driving, insights obtained from video data are indispensable.
For example, in anomaly detection within the manufacturing industry, training AI models with video annotation data allows for the real-time identification of product defects or problems on production lines.
Furthermore, in the field of autonomous driving, video annotation is important for improving the ability of vehicles to accurately recognize traffic signals and signs and perform appropriate actions. The higher the precision of video annotation, the more accurately and effectively these AI models may be able to execute tasks.
Video annotation is extremely valuable for training AI models to predict the actions of people and objects. By utilizing annotation data, AI can learn patterns of human and object movement and become able to predict future actions.
For example, by training an AI to detect dangerous movements or precursors of criminal activity, it can warn of danger in real-time and mitigate the risk of accidents and crime. This becomes an important element in strengthening public safety and corporate security.
If actions you want to detect are learned in advance through video annotation, analysis and processing are possible even in real-time. Real-time processing, due to the evolution of AI technology, is bringing a revolution to the areas of crime prevention and surveillance.
AI models trained through video annotation enable real-time video analysis and can instantly identify anomalies and dangers from surveillance camera or live camera footage. The application of this technology has the potential to complement human monitoring and, in some cases, replace it.
Improving the precision of real-time processing will enhance safety and reliability and speed up response. Applications are especially expected in high-risk environments requiring strict monitoring and in areas where large-scale monitoring is difficult.
Video contains information on how a target object has moved. Therefore, even if a part of the target object is hidden in the video, its identity can be recognized from the preceding and following information.
This is a merit unique to video that can utilize preceding and following information, a function not found in image recognition models. Because more target objects can be recognized, more detailed situational awareness becomes possible.
3. Types of Video Annotation
Since video annotation is a series of image annotations, the same techniques as image annotation like the following are often used.
| - Classification - Bounding Box (Rectangular) - Polygon (Multi-sided) - Semantic Segmentation - Landmark |
In classification, the situation of the video is judged and a label is applied to the video as a whole. If classifying by "weather," labels such as sunny, cloudy, or rainy are applied.
Bounding Box (Rectangular) and Polygon (Multi-sided) involve enclosing a target object in a specified frame and labeling it. If the position of the target object changes in the video, the frame is moved accordingly.
Semantic segmentation labels per pixel of the frames that constitute the video. Labeling all pixels requires great effort, but enables high-precision annotation.
| Read also: What is semantic segmentation? Explaining types, methods, and image processing application examples! |
Landmark labels the keypoints of target objects. It is often used for the recognition of target objects whose keypoints change by individual, such as body type or face.
The reason why choosing an annotation tool is important in annotation work is that while annotation is becoming possible to do automatically, not everything is automatic.
The tool is merely an auxiliary tool for performing annotation efficiently, so if performance is poor, the quality of the AI model will drop, and if usability is poor, work efficiency will drop.
Here, we explain the points to look for when choosing an annotation tool.
| ① Purpose ② Annotation Functionality and Usability ③ Task Management |
Each video annotation tool has strengths and weaknesses. Be sure to choose the tool most suitable for your company's purpose.
For example, if you use a tool whose strength is landmarks to perform annotation using bounding boxes, you will not be able to make the most of its strengths. Furthermore, if you choose a low-performance free tool to suppress prices and it does not reach the required quality, the work will all be wasted.
To avoid such situations, please clarify the purpose of annotation and the required precision.
In addition to basic performance, functionality and usability required for performing video annotation should also be checked. Below are some useful functions.
Frame interpolation is a function where if you label the first and last frames, the frames between them are also automatically labeled. It is a very important function because its mere presence makes annotation work efficient.
The comment function allows for commenting on annotation data, realizing smooth communication regarding annotation instructions, specification transmission, corrections, etc.
The test function can measure whether annotation workers possess the necessary technical skills.
Also, check whether these functions are designed to be easy to use. If usability is poor, work speed will drop. To increase efficiency, choose an annotation tool with good usability.
If task management can be done quickly, the burden on managers can be lightened. Depending on the number of target objects and the check system, the process of annotation can become extremely numerous and complex.
If an annotation tool has progress confirmation and schedule management functions, it will automatically summarize the management status, reducing the manager's burden. If adhering to schedules is strict, or if processes are numerous and complex, try to choose a tool that is easy for task management.
To state the conclusion, it can be said to be possible to do in-house if there are professional staff who can handle data correctly and are proficient in using the tools. We explain this reason from the quality and price aspects.
The quality of annotation directly relates to the performance of the completed AI model. Therefore, if high-quality annotation can be performed, it may be done in-house.
However, you will not become able to perform high-quality annotation just by using a tool. The tool is merely an auxiliary tool for performing annotation efficiently.
At the current stage, humans can annotate more accurately than tools. In addition, the selection of data, which significantly affects quality, must be done by humans.
There is a tendency to have an image that outsourcing will be more expensive, but in-house production can also be more expensive. Certainly, requesting outsourcing incurs a request fee, but in the case of in-house production, the following costs are incurred:
| - Tool costs - Labor costs - Education costs |
Annotation takes an enormous amount of time. Since many personnel must be allocated to annotation work, labor costs are necessary. Furthermore, education is also necessary before work. If there are no people familiar with data, studying AI models and machine learning will also be necessary.
Furthermore, if the required performance cannot be obtained with the completed AI model, it may result in starting over in the worst-case scenario. As such, there are risks in the in-house production of annotation.
In contrast, if you outsource, although it costs more than in-house production, high-quality annotation data can be obtained with less workload. Since employees can concentrate on core business accordingly, it may be a plus when viewed comprehensively.
Since which one becomes more advantageous differs by company, please consider whether outsourcing or in-house production is suitable for your company.
In video annotation, in addition to the recognition of target objects, the movement of objects and even their direction can be recognized, so the fields of application expand remarkably.
However, a corresponding large amount of data needs to be annotated. When performing annotation work, please try to consider if ingenuity can be applied so it can proceed as efficiently as possible.
Furthermore, to suppress costs, it is also important to use outsourcing and in-house production selectively. Please make the optimal choice according to your company's situation.