Video Annotation for Intelligent AIs
Label and prepare training data with Video Annotation Services for Computer Vision
Why is Video Annotation Services needed for Computer Vision?
Have you ever considered how AIs, ML setups, and machines based on computer vision can proactively identify video-specific entities and take actions, accordingly? This is where video annotation comes in, allowing intelligent systems to recognize and identify objects, patterns, and more, based on the labeled data fed to them.
Still unsure about why video annotation makes sense! Well, if you have ever considered owning a self-driven car, knowing the nitty-gritties of video annotation makes complete sense. Be it training autonomous vehicles to detect roadblocks, pedestrians, & obstacles are good at determining poses and activities, video annotation has a role to play in training almost every perceptive AI model..
If you are still confused as to how the entire premise works, here is a self-explanatory example:
Imagine training the knowledge database of a self-driving car before unveiling the prototype. To be able to function at top capacity, the autonomous vehicle should be able to identify signals, people, roadblocks, barricades, and other entities to drive through with accuracy and precision. However, this can only be made possible if machine learning & computer vision models can learn using the labeled data sets, eventually used to train the algorithms.
Video Annotation and Labeling – Human Touch for Your AI
Long story short — Shaip lets you access some of the most advanced video annotation solutions to ideate perceptive and highly intelligent models. As a video annotation company, Shaip lends the most effective model training firepower to your goal-specific setups, fortified further with data mining tools, in-house data labeling teams, and the ability to bring in a wide range of video annotation tools to suit every relevant use-case.
If you outsource video annotation requirements to Shaip, you can get your hands on the following resources:
- Ability to handle longer videos and extract info
- Automated annotation perspective for faster time-to-market
- Access to frame-by-frame labeling
- Industry-specific coverage
- Higher accuracy
- Ability to process insane volumes of data
Productive Video Labeling Made Easy
Capture each object in the video, frame-by-frame, and annotate it to make the moving objects recognizable by machines with our advanced video labeling services. We have the technology and the experience to offer video labeling solutions that help you with comprehensively labeled datasets for all your video labeling needs. We help you build your computer vision models accurately and with the desired level of accuracy. Define your use case and let Shaip do the heavy lifting of powering vision models, with the following tools at our disposal:
Arguably the most reliable video annotation technique, Bounding Box annotation concerns ideating imaginary rectangles to detect objects.
For scene and object classification, if there are irregularly shaped entities in play, polygon annotation comes in quite handy, as it is more accurate than bounding boxes.
If you want to develop more targeted and accurate computer vision AIs, you might want to consider semantic segmentation, which concerns classifying images at the pixel level.
Biometric security setups like face detection can benefit from Keypoint annotation that focuses on labeling user expressions, specific facial markers like lips, noses, eyes, and even annotation at the cellular level.
3D Cuboid Annotation
Probably a more defined version of the Bounding Box annotation, 3D cuboids are used to identify and label objects in three dimensions rather than two, as offered by 2D bounding boxes.
Line & Polyline Annotation
This technique is best deployed for verticals that require a more planar approach towards labeling entities. It is used for annotating pipelines, roads, rails, and datasets concerning road markings, lanes, and more.
For data workflows concerning YouTube video, we implement frame classification as the preferred way of annotation. This lets you make videos more navigable, with the ability to skip frames and exercise better control.
If you want better engagement on your videos, we recommend video transcription as a supplemental form of annotation, best suited for translating the audio snippets of the concerned video into text.
If you plan on developing models for security applications, fitness, and sports analytics, we recommend and deploy skeletal annotation for identifying and labeling data sets with a focus on body alignment and positioning.
For certain labeled categories, you need to fixate on sub-categories to taper down decision-making and make analysis even more accurate. Instance annotation, as a part of multi-label video annotation, helps you with the same by categorizing vehicles further as buses, cars, and more.
Video Data Analysis
In case you want to analyze the video annotation need before planning a full-fledged training strategy, you can always rely on our video data analysis that aims at helping you plan the use cases better, plan out highly specific goals, and eventually allow us to deploy the right annotation technique.
Once the video data analysis is over, we can even help you plan out custom annotation strategies, even if your use case is highly elusive and requires further detailing.
Reasons to choose Shaip as your Trustworthy Video Annotation Partner
Dedicated and trained teams:
- 7000+ collaborators for Data Creation, Labeling & QA
- Credentialed Project Management Team
- Experienced Product Development Team
- Talent Pool Sourcing & Onboarding Team
Highest process efficiency is assured with:
- Robust 6 Sigma Stage-Gate Process
- A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
- Continuous Improvement & Feedback Loop
The patented platform offers benefits:
- Web-based end-to-end platform
- Impeccable Quality
- Faster TAT
- Seamless Delivery
Why you should outsource Video Data Labeling / Annotation
Expert image data collection isn’t all-hands-on-deck for comprehensive AI setups. At Shaip, you can even consider the following services to make models way more widespread than usual:
We specialize in making textual data training ready by annotating exhaustive datasets, using entity annotation, text classification, sentiment annotation, and other relevant tools.
Labeling audio sources, speech, and voice-specific datasets via relevant tools like speech recognition, speaker diarization, emotion recognition, is something we specialize in.
We take pride in labeling, segmented image datasets to train computer vision models. Some of the relevant techniques include boundary recognition & image classification.
Buyer’s Guide for AI Training Data
In the world of artificial intelligence and machine learning, data training is inevitable. This is the process that makes machine learning modules accurate, efficient, and fully functional. Read the guide to understand the importance of high-quality data and how they affect your ML models.
Image Annotation & Labeling for Computer Vision
Computer vision is all about making sense of the visual world to train computer vision applications. Its success completely boils down to what we call image annotation – the fundamental process behind the technology that makes machines make intelligent decisions and this is exactly what we are about to discuss and explore.
Computer Vision Services & Solutions
Computer vision is an area of Artificial Intelligence technologies that train machines to see, understand, and interpret the visual world, the way humans do. It helps in developing the machine learning models to accurately understand, identify, and classify objects in an image or a video – at a much larger scale & speed.
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Video annotation is the process of labeling video-specific entities with relevant metadata, to make them training-ready and machine recognizable.
Labeling on-road entities like cars, pedestrians, street signs, and other elements for training self-driven cars, tracking and categorizing poses and facial key points for specific games and apps, and even tagging custom entities to speed up intelligent manufacturing are some of the examples of video annotation.
At present, you are advised to annotate YouTube videos by resorting to outsourced annotation tools like video transcription and frame classification. Unlike the annotation editor previously offered by YouTube, the outsourced strategies are expected to work better in improving user engagement.
Yes, you can annotate a YouTube video by primarily relying on frame classification and video transcription.
Vision AIs and models require truckloads of training data to learn from if you want them to be capable enough of taking independent and proactive decisions in the future. Therefore, computer vision needs properly prepared, tagged, and labeled video components to be fed along with algorithms to make the models and eventually the AIs, more perceptive.
Machine learning as a technology ensures that machines are capable of learning from identifiable patterns and data, sans human intervention. However, for this to be a reality, training-ready datasets must be fed to the system, which is best handled by video annotation.