Video Annotation is one of the annotation processes that require to label target objects in video footage. This information is generally added to videos by human annotators who apply outlines and labels to video frames in line with the specific requirements of each machine-learning model. In most cases, video annotation means teams of annotators locating relevant objects in each frame of video data.
Most commonly annotators use bounding boxes to pinpoint objects that machine learning engineers have designated as important to label. These boxes will then be assigned a colour and a label. Different machine learning projects require different ranges of objects to be labeled, in different ways.
Video Annotation for Machine Learning
While video annotation is useful for detecting and recognizing objects, its primary purpose is to create training data sets. When it comes to video annotation, there are several different steps that apply.
- Frame-by-frame detection – With frame-by-frame detection, individual items of interest are highlighted and categorized. By capturing specific objects, detection with ML algorithms can be improved.
- Object localization – object localization helps to identify specific images within a defined boundary. This helps algorithms find and locate the primary object in an image.
- Object tracking – often used with autonomous vehicles, object tracking helps detect street lights, signage, pedestrians, and more to improve road safety.
- Individual tracking – similar to object tracking, individual tracking is focused on humans and how they move. Video annotation at sporting facilities help ML algorithms understand human movement in different situations.
Use Cases for Video Annotation
RETAIL
Video Annotation is required to implement different retail applications like autonomous checkout , making predictions on whether a particular user will buy what product , what products are getting out of the stock in the store etc. These applications require them to classify and track actions of customers in stores, such as selecting products from shelves and placing them in baskets, returning items to shelf, attempting theft, keeping a count on the quantity of products present in shelves etc.
DRONES & AERIAL IMAGERY
Analysis and Annotation of Satellite imagery is widely increasing in different vertices of the AI industry.From Agriculture lands to Smart city management, application of drones and aerial imagery is expanding. For example Video annotation of aerial imagery is helping ML teams to detect and track suspects in surveillance videos, visually follow and manage fleets of logistics vehicles on site, urban area management or capture and analyze unsafe behaviors
ROBOTICS
Video Annotations help in enabling robots to navigate around stationary and moving objects, run video inspections on assembly lines, track and trace packages in warehouses and more. It can be used to train robots on sorting different raw materials and waste in the industry , barcode detection on packages, moving objects from one place to another and other such applications.
AUTONOMOUS VEHICLES
Video annotation is often used in the automotive sector to train machine learning algorithms that power autonomous vehicles. This is what allows self-driving cars to recognize thighs like street lights, other cars, pedestrians, street signs, and anything else they might encounter on the road.This powers models that capture unsafe driving behavior, track pedestrians and vehicles in traffic, or monitor passenger conditions
PRECISION AGRICULTURE
Annotating Videos of large agricultural lands assists in Advancing vision applications that track plant growth, enable navigation of autonomous harvesting machines, monitor movement of livestock, timely crop monitoring to check their ripeness, Weeds detection to help in spraying of pesticides in that specific areas only and many more.
SPORTS & FITNESS
Tracking human activity and pose estimation is used by video game companies to create the games we all love. This involves accurately annotating things like peoples’ facial expressions and how they and how they pose while performing various actions. Video Annotation can be used in analysis of sports matches and provide predictions for the future matches.It required to specify the timestamps of the events, the team name, event, comment, and other specific attributes.
TagX Video Annotation Services
Video annotation plays a crucial role in training computer vision models. However, segmenting a video into small frames and annotating a piece separately with the right metadata, unavoidable data quality compliances, inherent linguistic complexities, numerous probable classifiers, and volumes of data certain video contains is challenging. Businesses, therefore, outsource video annotation services to get excellent results timely and cost-efficiently.
TagX offers an efficient and accessible annotation framework that can be modified according to the deep learning model’s relevant use cases. Our professional annotators deliver the best-in-class results with the right blend of skills, experience and expertise. Apart from the frame-by-frame analysis of videos, detection and metadata annotation, and object recognition, we also provide rapid video annotation services for computer vision models.
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