Video Annotation Services
Services / Video Annotation Services
Video Annotation Services
aiTouch provides high-quality video annotation & labeling services for the most complex training data
Bounding Box
The bounding box is the most popular and commonly used video annotation method. It is applicable for a wide range of use cases like retail, robotics, and autonomous vehicles. Our experts use rectangular box annotation to illustrate objects and create training data, enabling trained algorithms to identify and classify objects during the ML process. We use 2D and 3D bounding box annotation tools depending on the quality and quality of the data.
Polygon Annotation
Polygon annotation allows all of an object’s exact edges to be annotated, regardless of shape. These annotate irregular objects within a video - asymmetrical things that don’t easily fit into a box, making 2D or 3D bounding box annotation techniques insufficient to depict an object in motion or its form correctly. Our annotators plot points on each vertex of the target object, allowing computer vision and other AI models to recognize and respond to them.
Semantic segmentation
Used when high precision is required, semantic segmentation ensures every video component belongs to only one class. Our team segments the video into parts and assigns categories (like a car, sign, bike, pedestrian) to classify objects pixel by pixel for creating high-performing data sets. It helps train AI models to recognize and classify specific objects into multiple formats, even if partially hidden or obstructed.
3D Cuboid Annotation
This technique involves labeling objects in a video using cuboids for processing 3D training data. aiTouch’s annotators use cube-shaped boxes to teach ML models to go beyond mere identification of an object and get a more realistic depiction and how it interacts with its environment. It enables an in-depth dimensional view (location, height, width) and builds a more comprehensive model.
Polyline Annotation
Polyline annotation helps build datasets for precision application models. Objects in a video are annotated by drawing an accurate contour around them. aiTouch's annotators create training datasets to train an ML model to identify physical boundaries to operate within. Polyline plays a significant role in the safe operation of autonomous cars, drones, or robotics. It is of great value for boundary recognition to annotate sidewalks, road marks, lanes, and other boundary indicators.
Landmark/ Keypoint Annotation
Keypoint annotation helps label facial/skeletal features (including facial expressions and emotions), automotive parts, etc. Our team outlines object and shape variations by connecting individual points across objects to identify and tag central points of interest within a landmark or key point. Landmark annotation is especially significant in face recognition.
Rapid annotation
aiTouch's video annotation platform utilizes video interpolation to annotate video footage rapidly and create topline video training datasets quickly for any AI ML project. Linear interpolation and other cutting-edge annotation tools enable rapid labeling of moving objects across multiple frames, taking the model to the next level.
Object Localization for Computer Vision
A video has multiple visible objects, and localization helps identify the primary objects in an image. Identifying the main image and its boundaries are the chief objectives of object localization.
Tracking Human Activity & Understanding Postures
Video annotation involves training CV-based AI or ML models to track human activities and predict the poses. These are commonly used in areas with considerable human movement, like sports fields, to track athletes during contests and sporting events. Precisely labeled data that annotates the smallest details, like the facial expressions and specific postures while performing various actions, allow robots and automated machines to identify and learn about human activity and interactions in various situations and respond to them.
Object Tracking for Autonomous Vehicles
Video annotation plays a vital role in identifying objects for autonomous vehicles. Using annotated videos, autonomous vehicles can recognize objects like street lights, pedestrians, signboards, signals, cyclists, pedestrians, other vehicles, etc., on or around the road. Advanced video annotation tools can accurately label videos frame-by-frame to help developers build visual perception AI models, which further helps in building fully functional and reliable autonomous vehicles.
Why aiTouch
Training machines to understand and correctly interpret the visual world requires a high volume of precisely and accurately labeled training data. Experience, expertise, and access to state-of-the-art tools are crucial as AI programs can function optimally only with concisely labeled data. Data that is customized to your project and specific data training needs. Data that delivers the best cost: quality ratio. That’s where we come in.
aiTouch is your one-stop solution for all your data-related needs, from bounding boxes, polygons, and landmarking to semantic segmentation and panoptic annotation. We provide high-quality annotated video data for object classification, detection, localization, and segmentation in various use cases. We tailor our specialized portfolio of end-to-end annotation services and solutions to cater to your AI model training needs. Our highly skilled data annotators apply best practices and in-house next-gen video annotation & labeling tools to deliver world-class training data to our clients worldwide. We combine people and technology to create data that powers AI and automation while maintaining complete data security and confidentiality.
Industries We Cater To / Domains That Need Video Annotation Services
Organizations working on AI ML-based business models can leverage our quality and customizable video annotation services. These could be spread over many domains, from e-commerce, retail, healthcare, automotive, geospatial to agriculture, security, manufacturing, robotics, and many more.
Retail
Optimized training data for AI & ML has made imagining innovative consumer experiences in the retail space more plausible. Vision-based inspection allows deep learning of consumer behavioral patterns, making it possible to predict the type of product to pitch successfully.
E-Commerce
Video annotation in e-commerce enables experts to categorize content by multiple attributes, significantly improving online shoppers’ search relevance and customer experience.
Healthcare
Healthcare AI programs supported with annotated video data help device manufacturers, pharmaceutical companies, and healthcare providers leverage machine learning to deliver cutting-edge services. Besides, annotated data helps quickly identify patients' current medical requirements and future health risks, revolutionizing how medical diagnosis and treatment are carried out.
Automotive
As the concept of smart cars and autonomous vehicles gains momentum in the automotive sector, robust AI programs like video annotation create training data that can help deploy autonomous technology. The data can help detect and differentiate images like signboards, signals, vehicles, streetlights, and other objects on the road, to make travel safer and hassle-free.
Geospatial Technology
Video annotation helps gather intelligence from aerial, drone, and satellite footage to power applications in agriculture, logistics, energy, security and weather forecasting, traffic navigation, etc.
Robotics
Computer vision supported with reliable video annotation holds the key to automation in the real world. Trained AI ML models infuse robotic process automation with intelligence to carry out operational tasks more effectively and efficiently.
Manufacturing
From sorting inventory through computer vision to 3D cuboid annotation for robotics process automation in product packaging, trained AI programs are increasingly reducing human efforts at all stages. For example, video annotation makes manufacturing processes more efficient by increasing the efficiency of industrial robots.
Finance & Insurance
Video annotation is crucial for agile security. It assists processes like night vision, crowd detection, thermal vision, traffic motion, face identification, pedestrian tracking, theft detection, etc. Using labeled videos, annotators create datasets for video equipment to provide more comprehensive security.
Security & Surveillance
Image annotation is crucial for agile security. It assists processes like night vision, crowd detection, thermal vision, traffic motion, face identification, pedestrian tracking, theft detection, etc. Using annotated images, annotators create datasets for video equipment to provide more comprehensive security.
Agriculture
AI-trained robots, drones, and machinery help farmers protect their crops with minimal human intervention. Video annotation in agriculture helps in livestock management, geo sensing, crop health monitoring, plant fructification detection, and unwanted crop detection, to name a few.
Government
Video annotation offers the ideal solution for handling sensitive data that requires secure processing at various levels, central, state, or local, like facial recognition, etc.
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