Video annotation makes up a sizable percentage of the worldwide data annotation industry, which is expected to expand from $1,186.9 million in 2021 to $13,287.9 million by 2030. Similar to image annotation, video annotation is one of the key techniques researchers use to tag video clips with informational metadata and create a dataset that is used to train computer vision models. Currently, many businesses and industries are reliant on video annotation to train their AI/ML models for various purposes. Among that lot, this post will examine the most critical use cases for video annotation in businesses.
Importance of Video Annotation & Its Techniques
Each object in a video is identified, noted, and categorized on a frame-by-frame basis via video annotation. The basic goal of video annotation is to simplify AI-powered systems to identify things in videos.
To create a high-quality reference database that can precisely identify objects like people, cars, and animals, computer vision-enabled systems can use correctly annotated films. It is impossible to overestimate the importance of video annotation, given how many commonplace tasks are now dependent on computer vision.
The performance of the AI model will increase with the accuracy of the video labeling. With the use of automated technologies, precise video annotation enables businesses to deploy with confidence and scale up swiftly.
There are two different approaches to approach video annotation that provide very different outcomes. Let’s take a look at them below:
1. Single frame Annotation
The video is divided or separated into different frames or images for this type of video annotation. This method can be time-consuming and expensive and is only appropriate for videos with less dynamic object movement.
2. Multi-frame Annotation
Also known as continuous frame-by-frame data annotation, the annotator labels the objects as video streams using data annotation tools. To assess the pixels in the previous and subsequent frames and forecast the motion of the pixels in the current frame, computers rely on continuous frame techniques like optical flow. With this amount of information, machines can correctly identify an object that is visible at the start of the film, then vanishes for several frames before reappearing later.
Uses Of Video Annotation In Various Major Businesses
Video annotation is being widely used by a multitude of businesses to bring enhanced accuracy to your AI’s model predictions. Some of the applications of video annotation are as discussed below:
1. 1. Movement Detection in Sports
Although it might seem improbable, professional athletics are discovering a need for keypoint annotation, a subset of video annotation. Some companies are monitoring player movement using this technological framework, noting performance improvements through video clips that would otherwise go unnoticed by the naked eye. Additionally, a tiny alteration in how your muscles move could be a sign of an impending injury. Finding it early on helps with prevention and may help a player’s career last longer.
The use of video annotation by coaches to assess a player’s strengths and weaknesses aids in recruiting and evaluation. By using keypoint annotation to train a model, it is possible to predict a player’s motions and gauge their degree of expertise. The information is then saved and compared to that of other players to create a fair representation of the candidates’ skill levels. In order to gain a general idea of what a team would look like, coaches can evaluate a player’s strongest traits and compare them to those of the rest of the squad or to those of other potential players. Additionally, it aids in identifying players who have not yet succeeded in the major levels.
2. Detecting Ailments in Healthcare
Movement analysis and keypoint annotation go beyond sports. When using keypoint annotation models for movement analysis, medical professionals can learn a lot about a patient’s health, particularly by analyzing their gait. In order to learn more about different diseases and injuries, scientists are employing their understanding of gait measurements and analysis, and they are using machine learning to do so.
Medical practitioners can frame-by-frame mark the primary joint regions in patients with illnesses including stroke, Parkinson’s, and cerebral palsy by categorizing digital videos using video tagging. Scientists can assess a variety of metrics, including walking speed, cadence, swing and stance time, and double support time, by applying the idea of transfer learning to their data and putting pre-trained models into practice using deep learning. They were subsequently able to compare gait data in pretrained models with those of healthy people using their findings. It is simple to obtain a general understanding of what these metrics should resemble because healthy individuals frequently have comparable gaits.
3. Protecting the Crops & Livestock
Computer vision models are helping farmers in a number of ways, from livestock and aquaculture to crop and produce monitoring. On the other hand, creating such apps demands working in unexpected, chaotic, and extremely dynamic environments where terrain and the targeted items are always changing.
Using high-definition video recordings from aerial drones or sensor systems, farmers can construct field maps covering large regions of space. Technologies using artificial intelligence (AI) can identify areas that need immediate attention. By using resources more effectively, farmers may save money while also benefiting from increased crop yields.
Annotation has developed to track livestock production systems and, if possible, increase their effectiveness. Animal behavior classifiers use videos to analyze an animal’s movement patterns to estimate how much stress it may be under. For instance, weight forecasting algorithms can estimate an animal’s weight 150 days in the future, enabling farmers to alter diets well in advance.
Further predictions about illness susceptibility, weight loss, and other related impacts are guided by stress forecasts.
Labeled recordings of plants at various stages of maturity allow machines to recognize and notify farmers when it is time to harvest in addition to monitoring the health of livestock.
4. Understanding Shopper’s Behavior
To develop a variety of retail applications, such as autonomous checkout, predicting which product a given user will buy, recognizing which products are running low on stock in the store, etc., video annotation is required. These programs allow business owners to categorize and keep an eye on consumer behavior inside of their establishments, such as selecting products from shelves and placing them in baskets, returning items to shelves, attempting to steal stuff, counting the quantity of items on shelves, etc.
Retailers can also use AI-enabled cameras to analyze customers’ attitudes and preferences through videos while assisting customers with simple checkout procedures and product tracing. But for machine learning algorithms to work flawlessly, proper training data must be used in AI-based retail systems (apps & machines).
5. Drones & Aerial Imagery
A computer vision-based AI drone has surprisingly changed the tech landscape to a different level. However, it still needs lots of training data to visualize the objects while flying in midair and to avoid collisions.
Video annotation for drone training aids in the identification of moving objects while the aircraft is in the air. Drones can only distinguish people running, moving cattle, or fast-moving cars if they have been properly taught with the training data produced by picture annotation services.
With frame-by-frame annotations, video annotation is frequently used to identify the important items in a video and makes it possible to identify even moving things. It will eventually avoid catastrophic collisions in the air while facilitating safe flying in the long run. A machine-learning system can accurately label videos in order to interpret the data and detect objects, identify humans or other behaviors, and train the AI model for the drone.
For all kinds of methods to find or identify the many kinds of items seen from the aerial perspective, video annotation for aerial images is offered.
When it comes to aerial photography, experts can utilize it to rapidly and precisely analyze thousands of hours of satellite data on their own. AI systems are able to compare video data from various days and determine which structures workers have added recently.
The Bottom Lines
Video annotation is an important and excellent tool with many benefits for businesses and society. While many businesses have already implemented video annotation, the list of applications for this technology is constantly expanding, and new applications for computer vision are regularly identified.
The ideal video annotation tool platform would unite top-tier data professionals with automated annotation technology to supply the data required for your production processes. There is nothing wrong with seeking professional assistance. A group of specialists will work on your particular business needs by providing the finest solution for you.
