Video annotation services

Using labeling and tagging, you may teach your computer vision models to recognize things through video annotation. Every video frame must have an annotation in order to identify things in videos. The length and frame rate of the video will determine how many frames need to be annotated. Depending on methods and applications, Video Annotation provides contextual information.

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Types of video annotation

Object detection

Annotating objects of interest within video frames with bounding boxes or polygons, enabling precise detection and localization of objects for tasks such as object tracking and surveillance.

Action recognition

Labeling human actions and activities depicted in videos, facilitating the development of AI models capable of understanding and predicting human behavior in various scenarios.

Semantic segmentation

Segmenting video frames into semantically meaningful regions allows for pixel-level classification and understanding of scene composition and context.

Temporal annotation

Annotating temporal events and activities that unfold over time, enabling the analysis of dynamic sequences and temporal relationships within video data.

Emotion recognition

Annotating facial expressions and emotional cues portrayed by individuals in videos, empowering AI systems to recognize and respond to human emotions in real-time applications.

Steps in video annotation

Step 1: Data preparation

We begin by acquiring the raw video data and preparing it for annotation. This may involve pre-processing steps such as format conversion, resolution adjustments, and data augmentation to optimize the quality and diversity of the dataset.

Step 2: Annotation tool selection

Next, we carefully select the most suitable annotation tools based on the project's objectives, data complexity, and client preferences. Our arsenal includes a variety of tools ranging from manual annotation platforms to semi-automated solutions, ensuring flexibility and efficiency in the annotation process.

Step-3: Frame-by-frame annotation

Each frame of the video is meticulously analyzed and annotated by our team of skilled annotators. Whether it's bounding boxes, polygonal shapes, key points, or semantic segmentation, we ensure that every object of interest is accurately labeled with the utmost precision.

Step 4: Quality assurance

Quality control is paramount in our annotation process. We conduct thorough reviews and validations to ensure consistency, accuracy, and completeness across the annotated dataset. Any discrepancies or errors are promptly identified and rectified to maintain the highest standards of data integrity.

Step-5: Output delivery

Upon completion, the annotated video dataset is delivered to the client in the desired format, ready to be utilized for training and testing machine learning models. We provide comprehensive documentation and support to facilitate seamless integration into your computer vision pipeline.

Why should you outsource RND Softech's video annotation services?

Cost-effective solution for video annotation needs.
Access to skilled annotation specialists without the hassle of hiring and training.
Scalability to handle fluctuating annotation demands.
Focus on core competencies while outsourcing non-core tasks.
Faster turnaround time with dedicated teams working on annotations.

Utilization of advanced annotation tools and technologies.
Quality assurance processes ensure accurate annotations.
Flexibility to customize annotation services according to specific project requirements.
Reduced infrastructure and operational costs compared to in-house solutions.
Improved efficiency and productivity in video data analysis projects.

At RND Softech, we offer a comprehensive collection of Video Annotation Services customized to meet the diverse needs of our clients across industries. With our expertise and dedication to excellence, we are committed to helping you unlock the full potential of your computer vision projects.