Businesses have been great beneficiaries of videography since its invention. With it, they can market their services and products to their target base in many creative ways not possible via other media like print. Video has found even greater value in the digital marketing age, with 94% of marketers saying that video has helped them improve product/service understanding among prospects.
And now, the application of Artificial Intelligence and Machine Learning is taking video to a whole new level as a potent tool for business growth, thanks to video annotation. It is the technique by which computers can identify target objects in videos to accomplish the business objectives of a company. If you’re looking to take your B2C company to the future successfully, then video-based AI models driven by the most accurate annotation practices are a must.
This article elaborates on how video labeling and annotation can impact your business due to its many advantages like improved customer service, future readiness, beating the competition, etc.
What Is Video Annotation And Labeling?
Annotation is the method by which developers train AI/ML models using large samples of tagged data. Video annotation refers to the specific case of these professionals marking important objects in video data. It can be considered an extension of image annotation in which annotators tag objects in each image. Except here, every frame of the video data is treated as an individual image.
The ML/AI model learns to discern the target object from other unimportant data around and behind it. After consuming large volumes of annotated data, the model can identify the target object in new, unannotated data. This is the purpose behind annotation. The model can even annotate and present the label of the identified object in videos, This is called video labeling, and it is useful for training ML models by themselves instead of using manual annotation.
The B2C Applications Of Video Annotation
Using AI and ML models has become inevitable, and by extension, the use of video annotation to develop them, in a world inundated with Big Data. And the use cases for this annotation type are growing with the expansion of AI/ML into more aspects of the business.
Here are some of the ways video annotation and labeling can aid your B2C company in its functioning.
Security and Safety of Video Annotation
Using cameras and algorithms to identify faces as ID markers is not new in the field of AI. However, it becomes challenging when having to do it using video data as the faces won’t be still. The algorithm has to read a face dynamically, sometimes in real-time, even if that face is partially obscured or is looking away from the camera.
A prime example of this situation is a security system. There will be many people passing through the visible region of a camera simultaneously performing many types of actions that may render correct face reading difficult. Besides, the lighting conditions may also not be conducive to accurate facial recognition. There are ongoing efforts to make the AI models behind these systems more accurate using video annotation.
Developers virtually reconstruct a face using many markers at various points on it and drawing polygons by joining those points using lines. The algorithm is then fed the annotated data multiple times using hundreds of thousands of such annotated examples. Once it can label faces by itself, recorded video data is directly fed to it to identify faces. The process is repeated until the desired degree of identification accuracy is obtained.
Thus, your company becomes safer due to the security AI algorithms made possible by video labeling and annotation. Unfamiliar faces can get flagged by your security systems, and your security team can be notified of them. This annotation can be extended to detecting unusual behavior, too, by training the models to read the body language and movement direction of subjects. In this case, the entire body has to be annotated.
Motion Detection and Tracking of Video Annotation
The necessity behind the invention of the video was the lack of ability for images to show continuous motion individually. Thus, any video-related AI model should be able to detect motion and track an object if needed. This requires the model to identify the positional displacement of the target subject/object or the entire frame in some cases.
Using image annotation alone fails here as every image will be treated as an individual entity unrelated to the next. This makes it impossible to recognize the target object’s displacement occurring in them. However, video annotation can accomplish this. What’s more, the detected object can be tracked using annotation to distinguish it from the rest of the elements in the video data. Methods like facial recognition, heat signature detection, GPS, etc., can be used depending on the situation.
Vehicle Tracking of Video Annotation
A relevant use case for this is driving speed detection and vehicle tracking. Cameras placed at important locations like highway sections monitor the speed of the vehicles passing through. If one is found to be overspeeding, the AI behind it is alerted, and it tracks the vehicle from the point of detection to the point it reaches the legally permissible speed limit.
It can simultaneously record the video and alert concerned authorities about it, providing the video as proof. All of this requires video annotation and labeling to accomplish as the system must identify different cars of the same make, displacement change rate to calculate speed, etc.
Motion detection and tracking also find important use in gesture-based device controls. Various gadgets today allow their users to control them via hand gestures done in the air. The AI uses the device’s camera to monitor the user’s hand movements, noting the movement of the figures.
It creates a virtual image within the gesture performed by connecting virtually placed markers on various strategic points on the hand. If it finds that the newly performed gesture’s virtual image matches one on its database consisting of previously-recorded ones, it performs the action associated with that gesture.
Modern cars with screens containing the controls to many of its features like AC, internal light, music volume control, etc., use gesture-based control. It saves the driver from having to look away from the road to operate the unsafe screen. Some personal cameras use gesture-based control to take pictures with features like blink-to-click, smile-to-click, etc.
Object Color Identification
Color changes can tell much about an object/person, especially in real-time situations. A video-based AI/ML model should be capable of detecting such changes to monitor the required parameter accurately. Unlike the previous case, image annotation can be adapted to serve this function as the changes occur frame by frame. Video labeling, developed via annotation, helps here when there’s a need to detect color changes in moving objects.
A popular use case for color detection using annotation for video data is eCommerce. Online stores must process large numbers of parcels per day, all of which are tagged with Barcodes/QR codes for identification and tracking purposes. The AI used can capture the information in those labels while running over them in real-time by differentiating the light and dark segments. With correct color identification, it’ll know what a code label is and what is the rest of the box.
Another application of color detection is Augmented Reality. A smartphone can have an AI that discerns the various colors in a real-time video of its surroundings to check for various objects. The different objects can be used as reference points to create an internal map of the surroundings and help the user navigate to a particular destination (like with AR in Google Search), play AR games (like Pokemon GO), and other uses.
Storage space is always at a premium, especially considering the large video file sizes generated by high-quality video recordings. You need to be able to determine which video data is useful and which isn’t for deletion and space-saving.
With video annotation, you can do it easily. Automated video labeling can help the data management AI determine which video file contains the required data and pick it for saving while removing the rest. An example of this is a cloud storage service deleting video data based on upload date. If the user doesn’t log in frequently or doesn’t pay the subscription fee, then their data will be erased, starting with the oldest first.
Labeling and annotation for videos can also be used to categorize the files based on predetermined criteria. The proper categorization of data helps save space, remove duplicates, order the files correctly, and so on. It improves the overall quality of your data warehouse.
AI/ML for video data has moved beyond industrial applications and is making its way to everyday life at an increasing rate. Your company should be ready to embrace the new paradigm of consumer outreach for growth and brand recognition purposes. Video Annotation, along with video labeling, will pave the way to your success story by delivering accurately-trained AI/ML models for video data quickly for any use case you may require. Thai helps your company keep up with the competition, ensure future readiness, and provide great experiences to your customers.