Video Annotation Services

Video annotation is the process of labeling or tagging video clips to teach computer vision or AI models to recognize and identify objects. It involves tagging objects by frame so that AI/ML models can remember them. Training datasets are made by annotating high-quality videos for machine learning to work best. Deep learning can annotate videos in many fields, like autonomous vehicles, healthcare, and social media.

Computer Vision AI models need many training data to determine if they can make decisions on their own and proactively in the future. So, computer vision needs video segments that have been adequately prepared, tagged, and labeled so that algorithms can be used to improve the models and, eventually, the AIs.

5 common example of a annotation are:

• Bounding Boxes: Development of self-driving cars are an data annotation example. Klatch annotators marked vehicles, bikes, and people in traffic. The annotated objects are placed into a machine-learning model to help the self-driving car tell them properly.
• 3D cuboids: Klatch annotators draw a cube around the object of interest and put anchor points at each object’s edges. The annotator estimates where the edges would be based on the size and height of the object as well as the angle of the data.
• Polygons: Klatch annotators draw lines by putting dots around the outside edge of the object of interest. The space between the dots is labeled with classes, such as cars, bikes, or trucks.
• Semantic segmentation: Klatch annotators are provided datasets to label each structure with the correct name or to separate road infrastructure into vehicles, buildings, pedestrians, barriers, trees, and crosswalks.
• Polylines: Autonomous vehicle technology can be taught to stay in the right lane without turning by annotating polylines on the road lanes and sidewalks.

You can annotate a video file by:

1. 1. Determine what you want to annotate: You can annotate videos for personal or professional use. For a personal project, such as a YouTube video, you would like to make the video engaging and easy for the viewers to comprehend the various objects. Professional projects can annotate datasets to train an AI/ ML model.
   2. Purchase a costly video annotation tool and do it yourself or hire a professional annotation service like Klatch Technologies: There are many paid video annotation tools where you have to base on your skills and do the annotation yourself. This leaves a margin of error as you’re not trained in annotating. Your annotated videos may be inaccurate, poorly formatted, and low-quality. Instead, you can hire a long-term data annotation service provider such as Klatch Technologies, who will employ a professional annotation specialist to label your videos. Annotation specialists will use professional software to label and provide the annotated video per your requirement while you focus on your core business or research.
   3. Upload or review your annotated video: After annotating your videos, you can review the videos to check if they have been accurately labeled and can provide feedback. The whole process is conducted over a secure medium.

Klatch Technologies caters to all kinds of annotation or data labeling requirements for any project size.

The purpose of annotation for various industries are:

• Autonomous transportation and technology: Klatch’s image labeling helps build datasets for teaching self-driving software to recognize road signs, bike lanes, pedestrians, traffic lights, objects in the setting that could be dangerous, and weather conditions.
• Agriculture: Large farmers and agriculture companies use Klatch’s image annotation services to protect their crops from damage. Computer vision in agriculture helps keep an eye on crop health, detect weeds and pests, manage livestock and do geo-sensing.
• Security and surveillance: Klatch’s image annotation is used by companies that make security cameras and video tools to create training datasets for crowd detection, thermal vision, traffic motion, theft detection, and pedestrian tracking.
• Medical AI: Klatch provides medical image data to train ML models for pharmaceuticals, medical devices, and health insurance companies with HIPAA-compliant image annotation.
• Retail and E-Commerce: Katch delivers image labeling and computer vision solutions to retail and e-commerce brands to improve customer service, market research, inventory management, trend forecasting, and brand reputation.
• Financial services: Klatch prepares datasets for ML models and technology for insurance and financial services firms. The models help improve customer satisfaction and reduce policy claim time and risk assessment.
• Manufacturing: Klatch’s image annotation allows manufacturing companies to recognize robot productivity and maximize production efficiency. Intelligent robots are used to aid in the detection of faulty products or defects in manufacturing.

We assure your data protection by combining streamlined video annotation operations and cross-quality checks. The results are supplied in a secure manner tailored to your specific requirements. Our data annotation teams adhere to a stringent nondisclosure agreement.

Annotating video can be done in-house until there is too many videos to handle. When things get hard, you’ll need specialists and the correct tools to label them accurately. For example, if you are developing a medical product, you will need a subject matter expert to classify objects found on an MRI report. Klatch data specialists can annotate datasets in various processes.

At Klatch, we annotate by a specialized staff of qualified data annotators and subject matter experts who use a tried-and-true combination of manual operations to label data. Our team of annotators begins by comprehending the project specifications as laid forth by the client. As a result, they conform to the current annotation dashboard and terminology. To enhance the utility of training datasets, they use a customized technique to extract datasets and add suitable labels and attributes. What seperates us from the competition is that our founders handle your projects and give direction on formatting data.

Three types of annotation are:

• Text annotation
• Image annotation
• Video annotation

Klatch Technologies caters to all kinds of annotation or data labeling requirements for any project size.

The types of annotation we use are:

• Object of interest detection: Object recognition is a crucial use in data annotation. In this step, the object is identified and labeled. A data annotation method is used to annotate these objects and make them detectable using computer vision.
• Recognizing types of objects: It is essential to recognize what type of objects are identified. The diverse objects identified could be cars, bikers, street poles, sidewalks, pedestrians, trees, and buildings, as visible in the natural setting.
• Objects classification: Models for machine-learning training entail that objects be classified. Data annotation utilizes several distinct methods to help an AI model’s detection and categorization of objects.
• Semantic object segmentation: Segmentation annotation assigns each data pixel to a class. Data annotation allows semantic segmentation and helps segment items by category, placement, and attributes in a single class.
• Recognizing human faces: In data annotation, people’s faces are annotated from one point to another, gauging the dimension of the face and its various facial features to feed facial recognition algorithms.

Our corporate headquarters are in India. We have various offshoring centers in Asia and Africa. Our client support teams are available 24 hours a day, 7 days a week, and keep them up to date on the project’s status.