As we live in an age where artificial intelligence (AI) is taking over, a large volume of data is being produced daily. The need to collect, organize, and analyze various data is not just a trend; it has now become an essential part of our everyday lives, even if we’re not aware of it. This, consequently, has put data annotation in the spotlight.
Data annotation is a result of technological advancements wherein artificial intelligence is used to categorize and label given data. It has four main types, namely audio annotation, image annotation, text annotation, and the central focus of this article, video annotation. Let’s dive in.
What is Video Annotation?
Simply put, video annotation is the process of training a machine learning (ML) model to know and tag what objects are shown or can be seen in a video. It is a part of the broader field of AI—computer vision. This aims to train computers to imitate the human eye’s perceptual capabilities. The procedure for this involves annotating moving objects in the video, frame by frame, in order to make them identifiable for the computer vision model or machine.
Giving computers the ability to analyze the environment through videos opens the door for many industries to new fields of use. This type of data annotation process is mostly needed or used in sectors such as autonomous technology and transportation, commerce, geospatial technology, government, manufacturing, and medical AI.
Most famous example of video annotation that is used is the self-driving car, where cameras on the car collect video and image data that are processed by the annotation tools used to determine the car’s navigation decisions.
Types of Video Annotation
There are different methods utilized when it comes to utilizing video annotation. Here are the most commonly used types:
- 2D Bounding Boxes. Annotators manually draw rectangular or square boxes to recognize, classify, and categorize items in motion across video frames.
- 3D Bounding Boxes or Cuboids. This method is used to identify a moving object’s length, breadth, approximate depth, and position. Annotators draw the boxes and place anchor points on the object’s edges.
- Polygons. If 2D or 3D bounding boxes are insufficient to adequately describe an item in motion or its irregular form, this is utilized. Labeling items properly requires a high level of precision.
- Landmarks and Keypoints. Small items and shape alterations are typically detected by producing dots throughout the picture and linking these dots to construct an outline of the object of interest throughout each frame.
- Lines and Splines. Majorly used to teach machines distinguish lanes and borders, these particularly apply to the automotive industry. Annotators simply draw lines at the borders of places that the system must identify throughout the frames.
- Semantic Segmentations and Annotations. This is a detailed segmentation done at the pixel level. This means that every pixel in an image or video frame is matched to a class.
Video, according to Cambridge English Dictionary, is a recording of moving pictures and sound, especially as a digital file. Image, on the other hand, is a picture or photograph, especially one shown on a computer. With these definitions, we get the idea that video annotation and image annotation are different in terms of information in one unit of the given data—in this case, a video or an image.
In video, annotators can distinguish an object’s location, position, and movement. Annotated videos are done on a frame-by-frame basis. For image, only one frame is being annotated. Image annotation is a simple structure of data working with limited information per unit of data. Additionally, in video annotation, audios can also be utilized when studying and labeling data in video frames with audio-specific data. This process is called audio annotation.
In short, unlike image annotation, which provides limited data in a single frame, a video is more complicated but more defined in giving the data structure of the given information.
Video Annotation Techniques
A video can have a frame rate of up to 60 frames per second. It means that annotating a video can take a long time. Hence, specialized data annotation tools and techniques are required for video annotators to utilize. Annotating videos may be done in two ways: single image method and continuous frame method.
- Single Image Method. This is a traditional approach that extracts all frames from a video one by one and then annotates them using a standard image annotation technique, also known as single frame. It is a time-consuming process since a huge number of images must be annotated.
- Continuous Frame Method. Otherwise known as stream video, this method evaluates a series of video frames using modern features of data annotation tools. Computers can monitor objects and their locations automatically, frame-by-frame, retaining the continuity and flow of the information gathered. This approach is more practical than the first method because it allows annotators to label items as they move in and out of the frame. Thus, it aids machine learning models to become more precise.
What Are Video Annotations Used For?
The process of video annotation is utilized in many ways. Since we live in a fast-paced world, it’s undeniable that every industry is in need of quicker ways to get things done or get required information in a matter of seconds. That being said, we’ve listed below the industries where video annotation is growingly utilized.
Computer vision demonstrated in video annotation processes aids in knowing and understanding the growth of plants and livestock. It analyzes and makes the entire harvesting process and caring for livestock easier.
2. Regulatory Compliance
A feature of video annotation can ensure that any content going live on television or the internet conforms to the media policy, standard, or law mandated by the government. It can be done by scanning videos and reviewing it with annotated data.
Quality control is a very tedious task and AI systems can help manufacturing industries save cost, energy, and time. Annotating models can detect defective items made by the factory or inspect if safety measures are followed in place.
4. Medicine and Education
Computer vision and ML can strongly assist the healthcare and education industry. Healthcare professionals, teachers, and students will be able to do taxing tasks effectively through video annotation services or tools.
Using this annotation method, consumer behavior can now be quickly studied. Cameras in retailers can detect which items customers are picking or returning to the shelves, and they can even prevent theft.
As mentioned in the previous list, video annotation can prevent theft as it can help enhance security features of an organization. It is also used in digital gadgets such as facial recognition for smartphone locks.
This method is advantageous in the field of sports by analyzing game analytics and projecting results of future games. That is by evaluating annotated data from recorded videos.
Last but not the least is transportation. The use of video annotation in autonomous vehicle systems to generate self-driving automobiles is widespread. Aside from that, it can aid in the detection of traffic conditions and road accidents.
Advantages of Video Annotation
Through video annotation, ease of data collection can be achieved. There is a high number of frames per second in a video that provides more than enough data than a single image. Thus, this process gathers multiple frames to annotate and feed to a machine or model.
Not only that, it can also offer greater annotation context. As previously stated, this method can help identify the location or movement of an object; it is needed for better context, which a photo cannot provide. That is just one example presented where context can strengthen the effectiveness of the annotation procedure. Therefore, the capability to give better and accurate context will directly upgrade in training ML models.
Common Challenges of Video Annotation
Of course, it is given that all technological advancements come with difficulties. The complexity of data volume is a common challenge of video annotation. With large volumes of data, it can be tough to annotate. Since videos are a collection of images or frames, it contains a high amount of information that is difficult to interpret and understand. In addition, pixels in a frame might not be accurate to some models.
These challenges may result in low-quality annotation. This is why developers must design machine learning models that exceed the standard of computer vision. In this regard, video annotators can also improve their approach in doing their tasks.
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