After several previous sharings, more and more friends have shown strong interest in data labeling.
Some of the annotation tools we shared (Original: Dry Goods Post丨What are the useful tools for image data annotation ) can annotate videos, so what is the difference between video and image data annotation?
Today, Xiao A will expand and talk to everyone.
What is video annotation
Locate and track objects in a series of images in units of frames, and the labeled video data will be used as a training data set for training deep learning and machine learning models, mostly for training vehicles, pedestrians, cyclists, roads and other autonomous driving fields model. These pre-trained neural networks are then used in the field of computer vision.
In video data labeling projects, human labelers and automated tools are combined to label objects of interest in video footage. This labeled footage is then processed by an AI-powered computer, ideally using machine learning techniques to discover how to identify objects of interest in new, unlabeled videos. The more accurate the video labels, the better the AI model will perform.
The meaning of video annotation
(1) Video data annotation is an inevitable requirement to realize the video search function. The video data in the Internet is growing at an alarming rate, and there must be a new retrieval method to meet the user's video retrieval needs. The video data annotation is marked by means of semantics and content, which is conducive to video data search, management and collection.
(2) The video data labeling requirements are determined by the characteristics of the video data itself. Rich video data contains massive information, and its content is more abundant, intuitive and vivid, which is unmatched by other media types.
(3) Video data annotation is an increasing demand for video data application scenarios. Like image technology, video data can also be applied to Internet entertainment, smart home, smart medical care, new retail, security, autonomous driving and other fields. Moreover, compared with image data, image data is data at a point in time, while video data is a collection of a series of image data that is continuous over a period of time, expressing richer information, so it has wider applications Scenes.
Information that may be included in video annotations
The information contained in the video can be divided into the following three parts from bottom to top:
1) Perceptual feature information: In addition to the visual features of the image, such as color, texture, etc., the video also has features that represent motion information, auditory information, and text information.
2) Structural information: Just as a book usually has a table of contents to help people quickly browse the content, a video also needs to construct an effective table of contents. A video directory may include different levels of structural information such as shots and scenes.
3) Semantic information: mainly refers to the psychological reactions of concepts, events, understanding and perception that people have when they see a certain video.
Video annotation type
Video classification
It is common labeling, such as ancient times, games, adults, women, cities, long hair, etc.
Video RBI
That is, the video information prompt point is to set the display content according to the time point of the video, such as setting a dot at two minutes, with text or screenshots. For example: when the mouse moves to the white dot on the video playback bar, it will display the content played at this point. The system can mark the key content points of the video, so that users can quickly browse to the content they want to watch.
video tracking
Video tracking and labeling is to capture the video data according to the picture frame and mark the frame. The marked picture frames are recombined in order to synthesize video data to train automatic driving. Video tracking and labeling is mainly used to train the mobile tracking ability of automatic driving to recognize the target. , so that the automatic driving can better identify the target during the movement. As shown in the figure, in a picture frame extracted from a video, people and vehicles will be labeled.
Differences between video and image data annotation
Video annotation has many similarities to image annotation, but there are significant differences between the two processes that can inform your decision if your company is to choose between these two data types.
data
Videos have a more complex data structure than images. However, in terms of information per unit of data, video is more insightful.
Using video, the team can identify not only where an object is, but also if it is moving and in which direction. For example, an image cannot indicate whether a person is sitting down or standing up, but a video will.
Video can also use information from previous frames to identify objects that may be partially occluded, a feature that images do not have. Taking these factors into account, a video can provide more information per data unit than an image.
Annotation process
Compared with image annotation, video annotation is more difficult. Annotators must synchronize and track objects that are constantly changing state from frame to frame. To increase efficiency, many teams use automated process components. Today's computers can track objects across frames without human intervention, so whole video clips can be labeled with less human effort. The end result is that the video annotation process is often much faster than image annotation.
accuracy
When using automated tools to annotate video, there is better continuity from frame to frame and less chance of errors. When labeling multiple images, the same label must be used for the same object, but consistency errors may occur.
When annotating a video, a computer can automatically track an object across frames and remember that object with its background throughout the video. Compared with image annotation, this method has higher consistency and accuracy, thereby improving the accuracy of AI model predictions.
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Data is the "new energy" of the AI era. With the development of artificial intelligence and big data technology, the data labeling industry has also ushered in rapid development. Among them, the labeling data related to computer vision is in great demand and has received a high degree of attention. Therefore, A large number of data labeling engineers are required to work on data labeling, but at the same time, higher requirements are placed on practitioners in this industry.
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