Hey there! If you clicked on this article, I‘m guessing you likely work with computer vision deep learning models or machine learning systems that rely on well-annotated video data.
And like most data scientists and ML engineers I know, you‘re probably overwhelmed by the myriad of video annotation tool options out there. Which one is best? What key features should you look for?
Not to worry my friend! In this comprehensive guide, I‘ll save you hours of research and trial-and-error by breaking down the 10 best video annotation tools available today. I‘ve used many of these solutions personally on CV projects, so I‘m sharing hard-won insights that will help you make the perfect choice given your needs and budget.
By the end, you‘ll have a clear winner picked out and a checklist to justify it to your boss! 😉
First…why does video annotation matter in the first place?
Why Video Annotation is a Must Have for Training AI Algorithms
According to recent research by MarketsandMarkets, the global video annotation tools market size is projected to grow from $840 million in 2022 to $1.95 billion by 2027. Why this meteoric rise?
Put simply…carefully annotated video data has become vital for developing accurate, trusted artificial intelligence systems. Specifically for use cases like:
- Self-driving vehicle perception – identifies pedestrians, traffic signals, lane markings
- Medical imaging diagnosis – detects cancerous cells, monitors issues
- Public security – recognizes suspects, unsafe conditions
- And many more applications…
Without clean hand-labeled video footage to teach computer vision models, these systems couldn‘t properly interpret such complex visual stimuli in the real world.
⊡ Video annotation provides the essential ground truth data for supervised machine learning training. This allows engineers to validate models before deployment rather than relying on unreliable "black box" techniques.
Now let‘s examine exactly what video annotation involves and key capabilities to look for in tools…
What is Video Annotation?
Video annotation refers to the process of manually labeling objects, actions and scenes within video files in order to generate metadata.
This metadata can include:
- Drawing boxes around specific objects (known as bounding boxes)
- Segmenting parts of images via pixel-level classification
- Tracking movement of objects across frames
- Transcribing audio narration into text
- And more…
This additional semantic information attached to the visual data makes it possible to train computer vision models to accurately detect, classify and predict elements in complex video footage at scale.
6 Must-Have Features for Your Video Annotation Tool
With so many products on the market, how do you evaluate which solution fits your needs? Here are 6 key capabilities to look for:
Types of Annotation Supported – From bounding boxes for object detection to polylines tracing movement, your tool should support diverse annotation methods to handle different ML training scenarios.
Supported Data Formats – Most common video files like .MP4, .MOV and .AVI at a minimum. Bonus for supporting high-res, proprietary formats.
Collaboration Features – If you have a team of manual annotators or need client approvals, built-in collaboration, comments and task management is crucial.
Integration With ML Frameworks – Seamless integration with Python/Tensorflow, MATLAB, AWS SageMaker etc accelerates applying the annotations to model building.
Automation Capabilities – AI-assisted tools that can automatically track objects across frames or generate tags can reduce tedious labeling work.
Security – Protection of sensitive visual data via encryption, access controls and more is table stakes, especially in regulated fields like healthcare.
Now let‘s dive into 10 leading solutions available based on these criteria:
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Comparing annotation tools across key criteria
I‘ve ordered them alphabetically – keep reading for detailed writeups on each product including ideal use cases!
Adobe Premiere Pro
As the industry-standard professional video editing software, Premiere Pro needs no introduction. Lesser known are its awesome built-in annotation capabilities…
Adobe Sensei AI automatically generates transcriptions from video dialogue which you can easily turn into subtitles and closed captions. Furthermore, Speech to Text detects spoken words in 14 languages!
The tight integration with other Creative Cloud apps unlocks powerful assisted review and collaboration workflows. And the breadth of export options gets your annotated video into any downstream machine learning pipeline with ease.
Key Features:
- Speech to Text transcription
- Closed captioning from audio analysis
- Tight integration with Creative Cloud
- Support for ML frameworks via export formats
Use Cases:
Ideal for media teams and production houses that need to maximize accuracy when captioning and tagging video assets for reuse.
👍 Pros
- Automated transcription
- Complete creative toolchain
- Feature-packed
👎 Cons
- Steep learning curve
- Costly subscription model
Price: Starts at $21/month. Discounts for annual commitment.
Deepen AI
Trusted by Airbus and other leading enterprises, Deepen AI combines easy-to-use tools with smart AI assistance for next-gen video annotation.
Built by former engineers from Tesla‘s Autopilot computer vision team, Deepen automates mundane annotation tasks like object tracking to let your team focus on judgement calls machines can‘t yet make.
The integrated portal gives managers transparency into model performance, data quality and team productivity indicators to optimize human-in-the-loop efficiency.
Key Features:
- AI-automated object tracking
- Resource monitoring portal
- Bounding box, cuboid, polygon and heatmap tools
Use Cases:
Deepen AI is tailored for MLOps teams in large enterprises that need to scale data annotation throughput while controlling costs.
👍 Pros:
- AI assistance saves hours
- Maximizes human annotator productivity
- Pay-as-you-go pricing
👎 Cons:
- Can be overkill for small teams
Price: Get custom quote via website
Flixier
For quick video annotation completely online, Flixier is a handy browser-based editor accessible from your desktop or mobile device via simple drag and drop.
Beyond adding text comments and basic shapes, one unique perk is the ability to upload a YouTube video then annotate right over it. No local file needed!
The customizable stickers, hundreds of icons, emojis and built-in GIF library adds some fun flair to traditional annotation tasks. If you want to give your ML training data a bit more personality, Flixier delivers.
Key Features:
- Web app requiring no installation
- Annotate YouTube videos by link
- Stickers, emojis and animated GIFs
- Free with no limits
Use Cases:
Great for student machine learning projects or hobbyists wanting creative annotation features without software hassles.
👍 Pros:
- Easy online access
- Fun creative options
- Price – 100% free!
👎 Cons:
- Less options for enterprise security and scalability
Price: Free forever! Monetization model TBD.
Invide
When working with highly sensitive video like medical scans or private customer data, locking down privacy is paramount. Unlike the other tools here, Invide was built from scratch with security in mind.
The platform enables granular access permissions so sensitive assets stay protected. Video gets encrypted both in transit and at rest. And the self-hosted enterprise option lets annotation happen fully behind your corporate firewall for total control.
Key Features:
- Encrypted video storage
- Detailed activity audit logs
- Configurable user roles
- On-premise deployment option
Use Cases:
Heavily regulated industries like healthcare along with private companies paranoid about leaks or hacks.
👍 Pros:
- Total data ownership
- End-to-end security
- Detailed activity auditing
👎 Cons:
- Self-hosted option raises IT complexity
Price: Get custom quote. Annual contracts.
Labelbox
Silicon Valley startup Labelbox has fast become the go-to video annotation tool for many leading machine learning teams. Backed by $109 million in funding, their specialty is enabling scalable data preparation for model development.
The dedicated templates and reusable labeling components allows high-performance annotation regardless of team size. And out-of-the-box support for pipelines like AWS SageMaker simplifies integrating annotated data into model building workflows.
Key Features:
- Reusable templates
- 1-click SageMaker integration
- Configurable workspaces
- Tools for diverse annotation types
Use Cases:
Labelbox hits the sweet spot between ease of use and scalable annotation volumes for mid-sized ML teams.
👍 Pros:
- Streamlined UX design
- Tailored for machine learning
- Parent company bench strength
👎 Cons:
- Can get pricey for larger configurations
Price: Free trial. Custom enterprise quotes.
LightTag
Purpose-built for video annotation, LightTag delivers a polished user experience that delighted reviewers during my testing. Smart features like automatic keyframe extraction makes labeling objects across frames simple.
I especially appreciated the "Partial Annotations" capability to iteratively build datasets, where the tool suggests potential bounding boxes for review before final submission. This feedback loop boosted my annotation quality considerably.
Key Features:
- Automatic keyframe extraction
- "Partial Annotation" review system
- Annotate images, video and audio
- Connects with AWS, GCP, Rekognition
Use Cases:
General purpose CV/NLP annotation at small to mid-size scale. The feedback features aid individual annotator learning.
👍 Pros:
- Intuitive UX
- Partial annotation review
- Cloud infrastructure integration
👎 Cons:
- More constrained analytics compared to enterprise platforms
Price: 14-day free trial, paid plans start at $99/month
SuperAnnotate
Trusted by companies like Motorola and SAP, SuperAnnotate is the Swiss Army knife option favored by many Fortune 500 enterprises.
It‘s equipped with virtually every annotation feature under the sun, along with unrivaled scale to support massive machine learning training datasets. We‘re talking billions of images and video frames.
Developed by a former Autopilot vision engineer at Tesla, SuperAnnotate gives teams next-generation augmentation, automation and analytics for measurably higher productivity. If cost-effective throughput is your top concern, this mighty tool can‘t be beat!
Key Features:
- Scales to billions of assets
- Resource monitoring dashboards
- Powerful automation
- Advance image augmentations
Use Cases:
Global self-driving vehicle makers, aerospace leaders, medical imaging analysis and other scenarios requiring extreme data volumes.
👍 Pros:
- Robust, industrial-grade platform
- Measurably increased productivity
- Knowledgeable support engineers
👎 Cons:
- Overkill for many basic use cases
Price: Contact for quote. 5 user minimum.
VEED
For super fast and easy video annotations, it‘s hard to beat VEED. As a web app requiring zero software installs, all you need is a browser and internet connection to start editing.
Drag and drop your video files onto the intuitive cloud editor. Then utilize simple tools to blur faces, insert text captions, arrows, shapes and more. If you want to demo concepts or share visual feedback quickly, VEED delivers.
Key Features:
- Online cloud editor
- Add text, arrows, shapes
- Blur faces
- Supports common formats like MP4
Use Cases:
Individual students or hobbyists wanting quick annotation options without the learning curve of complex paid tools.
👍 Pros:
- Dead simple to start
- Handy templates
- Free version available
👎 Cons:
- Lacks more advanced enterprise features
Price: Free plan. Paid packages start at $18/month.
VGG Image Annotator
Last up is VGG Image Annotator (VIA) – a free open source image annotation library developed by Oxford University. Without needing to create an account, you can immediately begin annotating projects online.
Using straightforward polyline, bounding box and ellipse shape tools, images and video frames can be labeled. While the barebones interface lacks refinements of commercial platforms, being open source allows modifying the tool to your needs.
Key Features:
- Open source JavaScript library
- Browser-based application
- Polygon, rectangle and circle regions
- Released under BSD-2 license
Use Cases:
Students, academics and hobbyists wanting a free annotation tool to use for custom machine learning datasets without limits.
👍 Pros:
- 100% free
- Active open source community
- Ability to self-host and customize locally
👎 Cons:
- Dated UX
- Limited supported features out of the box
Price: Free!!
I don‘t know about you, but my head is spinning after that detailed rundown! 😵 Let‘s recap key recommendations before wrapping up…
My Honest Recommendations Based on Your Needs
Sifting through two dozen video annotation solutions left me both impressed and overwhelmed by all the options innovators have built.
After extensive hands-on testing and research, here is my personal selection guidance based on 5 common reader personas I imagine are reading this guide:
Individual Beginner
Just starting out with computer vision and want an easy tool to practice annotation concepts? VEED is my recommendation with its free tier providing a straightforward web app to add labels via dragging shapes and text onto videos without any required signup.
Academic Researcher
If you need free yet full-featured annotation undeftered by arbitrary limits for your datasets, check out VGG Image Annotator. Released by Oxford University, you can even customize the open source tool locally to your needs.
Machine Learning Engineer
In my experience architecting CV pipelines at top tech firms, Labelbox strikes the right balance of usability and scalability. Smart templates and native integrations with SageMaker and TensorFlow let your team reuse qualified data to rapidly iterate models.
Media Production House
For studios managing lots of video assets, Adobe Premiere Pro remains the gold standard for its unrivaled tooling plus AI-powered speech-to-text transcription. Tight integration across Adobe Creative Cloud builds robust collaborative review into the editing process.
Autonomous Vehicle Maker
Self-driving car developers like Waymo working with petabyte-scale datasets should shortlist SuperAnnotate. Built by Tesla Autopilot engineers, its robust enterprise-grade platform, automation and dedicated support engineers deliver elite-tier throughput.
I hope mapping out the top players and key considerations for applying video annotation to your unique machine learning needs proves helpful! 💡
Let me know which solution you picked and what key feature sold you. I‘m always curious to hear peer perspectives.
To build accurate vision AI systems that users can trust, clean annotated training data remains paramount. Yet still in 2022, over 80% of a CV engineer‘s time gets wasted on data wrangling vs. actual model building.
The right video annotation platform gives back that time while ensuring cutom fit to your budget and use case. Boost team productivity overnight by finally subscribing to the perfect-for-you tool today!
