Computer vision models require high-quality training datasets with accurately labeled data to function well. But, video tagging in large volumes is more time-consuming and intricate (as opposed to image annotation) since it involves segmentation, annotation, and processing for overlapping frames. Performing video labeling in-house also poses challenges related to expenses, resource recruitment, training, and infrastructure. Businesses seeking training data for AI/ML models can easily overcome these obstacles by outsourcing video annotation services to Data-Entry-India.com.
Our team blends the right techniques, technology, and practices to annotate videos quickly and create efficient datasets. We have successfully delivered many training datasets for projects related to autonomous vehicles, robots, drones, face recognition models, and object/pose detection models.
As a trusted video annotation services provider, we meet client expectations and deliver on their requirements through high-quality solutions at cost-effective rates.
We employ multiple techniques for video annotation, depending on the type of frames and the client's annotation requirements.
We draw cuboids around objects in 3D images to define their length, width, and height. The technique is used in applications involving robots and drones to track objects along with the 3D space and the distance involved.
Bounding box annotation helps annotate objects in 2D images by drawing a box around them to define their appearance. We use this technique for image elements like pedestrians, lanes, potholes, signals, people, bags, animals, etc. We mainly offer 2D Bounding Box annotation services to assist self-driving models in easily recognizing their surroundings.
Under the keypoint video annotation technique, our team connects individual points (across targeted objects) to outline objects and shape variations. This helps in motion tracking, hand gesture recognition, and facial expressions/emotion detection.
Suitable for irregularly shaped objects, polygon annotation is used to precisely detect the shape and size of the annotated object. It also facilitates accurate localization and is majorly used in the automotive sector.
Line annotation is often used for linear objects like roads, pipelines, or wires. Video tagging experts locate markings and boundaries around a particular region. One of the most popular applications where we use this type of video annotation is in self-driving vehicles.
When we annotate video frames using segmentation, we essentially classify the images by adding a class label to every pixel. With semantic segmentation, we create machine learning datasets that establish the features of every object in a video and its subsequent representation, thus creating a meaningful connection that can be used for training purposes.
This video labeling technique involves locating, identifying, and tracking key points in every frame by tracking key points across every object in an image. For instance, for human pose estimation, we annotate facial expressions or major joints like the knee or elbow. For inanimate objects, we annotate corners or other tangible features.
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It involves locating the presence of an object in a video and showing its boundaries within each frame. We do this to find the most visible or the primary object in an image, thereby helping machines automatically recognize each entity within a frame.
It is a computer vision process of identifying and locating objects of multiple classes in a video. It involves explicitly drawing bounding boxes around each object to detect its placement. The human eye can classify different objects of interest within a matter of moments. Object detection helps replicate this intelligence for an AI algorithm.
Autonomous driving is the biggest use case of video annotation. Through precise video tagging and labeling, we help autonomous driving algorithms precisely detect and recognize objects on the road. We support the creation of training datasets that can enhance the capability of computer vision models in navigating through pathways and assist in predicting movement on a path and charting a course.
Our video annotation services for computer vision models are essential in preparing data for deep learning and object detection. We follow best industry practices and use effective techniques to reduce human labor time and efforts while preserving quality
Data-Entry-India.com is a 20+ years old data outsourcing company. We have supported a diverse clientele across the globe with standard and contemporary data-based support. Our video annotation services and data annotation services are aimed at new-age enterprises seeking support in advanced technology implementation.
We are equipped to process large data volumes, ensure accurate video annotation, and deliver projects quickly. Our resources become an extension of your in-house team and contribute to creating training datasets that reflect the environment where your AI/ML model will be used.
Join hands with us and leverage cost-effective support of the highest quality.
Hire our video labeling and annotation experts to reduce operational costs, mitigate bias from your training datasets, and amplify the performance of your machine learning models. To know more about our cost-efficient video annotation services, please send your queries at info@data-entry-india.com.
The most common video annotation challenges include:
Annotating a video requires a deep knowledge of annotation techniques and tools. You will need a learned team of data professionals who can annotate videos frame-by-frame and pixel-by-pixel. Even if you understand the process, this can end up consuming a significant chunk of your time and money while the results might not be as effective and reliable. Therefore, hiring video annotation experts is one of the most cost-effective ways to get desired results.
Yes. You can request a free consultation by either dropping us an email (with your requirements) at info@data-entry-india.com or ringing us at +1 585 283 0055 / +44 203 514 2601.
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