Image Automation and Assisted Videos Annotation

What's Pixel Perfect Semantic Sectionation?

Many methods of image annotation employ bounding boxes. However, some object detectors may be modified to produce segments. Pixel Perfect Semantic Sectionation maps every pixels and assigns an object classification and instance identity to each one.

Current annotation tools require researchers to work frame-by–frame in order to paint object information. Researchers might need to classify and identify objects within a few frames in order to get the required data. You will quickly realize how tedious this task can be when you consider the fact that a 1-minute video has several thousand frames.

Pixel-perfect semantic segmentation combined with AI Assisted annotation tools means that there are fewer frames to manually annotate. Innotescus proprietary ML modeling tracks objects and interpolates frames with no loss of accuracy. Researchers have a whole sense that spans tens to thousands of minutes of video, with only one segmentation in the beginning frame. This can lead to a 10x improvement in the labeled data while decreasing labeling time by minutes. As time goes by, the algorithm gets smarter and smarter to reduce the number frames required.

It can track movement and automate through frames. It can tell when an object is lost or if it leaves the screen. Segmenting objects at the pixels level allows for easier tracking and generates more Video Dataset. Al learns more from machine learning, and the algorithm does a lot of the heavy lifting in tracking objects' movements. It results in a dramatic decrease in frames that need manual annot. However, this does not affect the quality and quantity of data gathered.

The benefits of AI-Assisted Pixel Perfect Sectionation

Better data can lead to better insights, more efficient training and improved performance. Data engineers, data scientists, algorithm developers, and data annotators can make the entire process easier by using the right platform. The following benefits are available:

  • Elimination of repetitive labeling tasks
  • Convert raw data to labeled datasets
  • Reduce error space around objects
  • Create consensus labels from multiple annotations for the same image
  • Eliminate data-distribution biases
  • Increase the diversity of data
  • To achieve better outcomes, efficiently create Dataset For Machine Learning with enough complexity

The pixel-wise video annotation provides better data and deeper insights that can reduce much of the trial and error process involved in model design.

Video Automation With GTS

Global Technology Solutions (GTS) provides comprehensive computer vision solutions  by giving Dataset For Machine Learning along with Data Annotation Services, OCR Training Dataset and Audio Transcription Services to diverse industries including security and surveillance industrial, transportation smart cities, pharmaceuticals, and consumer electronics through the entire lifecycle of a model, including algorithm selection, learning and validation, through inferencing, deployment and maintenance.

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