Outsourcing video annotation has become a necessity for many businesses that need large-scale data labeling but lack time and in-house expertise. On the other hand, AI-powered video annotation tools promise efficiency but often fall short in accuracy, struggle with complex labeling scenarios, and require significant contextual training by subject matter experts.
At SunTec.AI, we bridge this gap by offering video annotation services with a human-in-the-loop approach. Our team of experienced annotators, robust quality control processes, and proficiency in widely used video annotation tools ensure consistency and efficiency in large-scale video data labeling. By combining the capabilities of automated annotation tools with our subject matter expertise, we overcome the limitations of both in-house annotation and AI-only solutions, ensuring your machine learning models are trained on precisely labeled data.
Leveraging video annotation techniques, such as bounding boxes and pixel-wise segmentation, we track and label the spatial location of objects of interest within video frames. Through precise object localization, we enhance the accuracy of computer vision applications used in autonomous driving, security surveillance, and retail analytics.
We utilize prominently used video annotation tools to recognize and classify objects frame-by-frame according to semantic categories. Through individual object delineation, we create reliable training datasets for applications used in self-driving vehicles, visual product search, medical imaging, and other operations.
Our subject matter experts can classify and label diverse activities such as object interactions, crowd movements, and significant occurrences within video footage. We track events across multiple frames and annotate them to train the AI model to identify similar events according to their classification.
Leveraging key point and landmark annotation, video labeling experts locate and annotate specific body joints and limbs, generating detailed skeletal representations of human figures. The labeled dataset can train applications for motion capture, augmented reality, fitness tracking, and human-computer interaction.
We understand that diverse video annotation projects require specific labeling techniques depending on their intended objective. By thoroughly understanding your AI model's goals and project requirements, we select the optimal video labeling method and provide reliable training datasets.
We draw 2D bounding boxes around the objects of interest in a video for efficient object localization and tracking. This technique is widely applied to create training datasets for applications used in facial recognition, retail inventory management, and wildlife monitoring. For more complex cases, we utilize the 3D bounding box (or cuboid annotation) technique. By drawing cuboids around the objects in three-dimensional space, we enable applications in autonomous vehicles, robotics, and augmented reality to accurately depict their width, length, and depth.
Our experts label each pixel in a video frame to categorize parts of the image into semantic classes for meaningful interpretation. By annotating multiple objects in a frame at the pixel level, we enable machine learning models to make more accurate predictions and better understand visual data for industrial inspection, environmental monitoring, and other use cases.
To label objects that do not fit neatly into rectangular boxes, such as vehicles, road signs, or human cell structures, we utilize polygon annotation. By creating precise outlines around irregular objects using multi-point polygonal shapes, we create training datasets for AI models used in geospatial analysis, medical imaging, and robotics.
We annotate facial features, such as mouth, nose, and eyes, as they appear in each video frame by placing and connecting key points. Landmark annotation is particularly useful in facial recognition, pose estimation, and security surveillance, helping AI systems precisely interpret facial expressions and body movements.
To label linear structures in videos, such as roads, lanes, and boundaries, our experts employ polyline annotation. This technique is particularly useful in creating training datasets for autonomous driving systems, enabling accurate lane identification and navigation along paths and roadways.
We specialize in labeling data points collected from 3D sensors like LiDAR. By annotating individual points or clusters in a three-dimensional environment, we enhance the spatial analysis capabilities of AI models used for urban planning, robotics, environmental monitoring, autonomous vehicles, and more.
Precision Farming: AI models can be trained on annotated field data to monitor crop health, detect pest infestations, and track growth stages
Livestock Management: By annotating animal movements and facial expressions in videos, AI models can be trained to monitor livestock behavior, detect health issues, and track feeding patterns
Medical Imaging: Annotating videos from endoscopic procedures and other imaging techniques to train AI algorithms for diagnostic purposes
Robotic Surgery: Annotating surgical procedure videos to train AI models for assisting in surgeries and improving outcomes
Object and Lane Detection: Annotating vehicles, pedestrians, traffic signals, lanes, and other objects in videos to create training datasets for ADAS (advanced driver assistance systems) applications
In-cabin Driver Monitoring: Annotating driver facial expressions and body posture in interior vehicle footage for training AI systems to detect driver distraction, drowsiness, and behavior in real time
Player Performance Analysis: Annotating sports recordings to train AI systems to track player movements and positions on the field and provide insights for areas of improvement
Injury Prevention: By annotating existing match videos, AI systems can be trained to analyze athletes' biomechanics and forms, helping medical staff/trainers identify potential injury risks
Quality Control: Annotating defects and anomalies in production line videos can help train industrial robots for quality assurance
Predictive Maintenance: Annotated videos of machinery in operations train AI models to spot early signs of wear or malfunction, enabling predictive maintenance
Process Automation : Annotating video footage of production processes can facilitate the training of industrial robots for task automation
Facial Recognition: Skeletal annotation of human faces in surveillance footage can facilitate the training of AI models for security applications
Traffic Monitoring: Annotated video data of traffic footage can also train computer vision models to monitor traffic flow, detect accidents, and identify traffic violations
Virtual Property Tours: Annotating features and details in property videos to train AI systems for enhanced virtual tour experiences
Geospatial Analysis: Satellite and drone footage of lands can be annotated to train ML models for monitoring environmental changes, land mapping, and more
Visual Search: Annotating product videos for specific features can improve the visual search capabilities of AI models used in eCommerce platforms
Customer Behavior Analysis: Annotating customer movements and interactions to enhance in-store experience and layout optimization
We understand your project goals and review the provided video datasets for preliminary processing.
We select the optimal video annotation tool based on project needs and configure it according to labeling protocols.
Leveraging automated tools and appropriate video annotation techniques, we label objects, actions, and scenes in visual data.
Through multi-level automated and manual checks, we validate the accuracy and quality of the annotated data.
The annotated dataset is shared with the client in the preferred format, and necessary adjustments are made based on the feedback.
Outsource video annotation services to us and improve the efficiency of your AI models.
Get human-validated training data for AIThe cost of outsourcing video labeling services to SunTec.AI varies depending on project complexity, data volume, and other requirements. However, we provide flexible engagement models to cater to diverse business requirements. Email us your project details at info@suntec.ai for a free quote.
We maintain regular communication through a dedicated project manager and weekly reports (highlighting project status, milestones achieved, and any challenges encountered). Furthermore, you can collaborate with our team via Zoom, Skype, Slack, or any other preferred communication medium for feedback and real-time adjustments.
If your requirements change marginally, we can re-annotate the already labeled datasets based on updated specifications. If requirements shift significantly mid-project, we assess the new requirements and provide a revised timeline and cost estimate. We also offer revisions free of cost for basic adjustments based on client feedback.
Yes! We have over 850 experienced in-house data experts to efficiently manage large-scale projects. Additionally, we employ advanced video annotation tools and automated workflows to maintain process efficiency and ensure timely project completion, regardless of the data volume.
We can start on a small batch of images or videos for free. No hassle and no commitment
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