Top Video Annotation Tools Compared 2022
The difficulty and repetitiveness of video annotation has been a struggle for the computer vision and machine learning communities for some time now. To meet these challenges, video annotation platforms need to do more than just annotate; automation, team management, and metadata are some of the strategies being used across the industry. As a result of this industry-wide feature fight, the video annotation marketplace is filled with diverse tools of varying quality.
In this blog, we’ll quickly explore annotation platforms and the features they offer to help improve the video annotation process. We’ll be looking closely at six big names in the video annotation market: Innotescus, Dataloop, Scale, V7, SuperAnnotate, and Labelbox.
What’s inside this blog:
- Why annotate video?
Why Annotate Video?
There’s plenty of reasons to annotate video. The flashiest example among them is autonomous driving – a fast-paced and video-driven product that responds to data in real time. Every frame counts. Computer Vision practitioners have a lot to gain from the dozens of frames per second video offers, and it ultimately helps training data look and act more like real world applications.
But annotating video is not without its challenges. While additional context from video can increase annotation accuracy (is the chair being pushed or pulled?) translating those insights into actionable data will take longer than if it were contained in a single image. Ultimately, to properly perform video annotation you need an efficient and thoughtful computer vision tool that uses automation in all the right ways.
Innotescus is leading the way in video annotation with its Automated Video Annotator (AVA™). AVA™ automates object tracking and empowers annotators to create more quality annotations with less manual work. Additionally, users can import video in almost any way they want, and export with just as many options.
What stands out about Innotescus is its ability to automate video annotation for all of its annotation offerings; object detection, instance segmentation, and semantic segmentation. Users can skip the frame-by-frame labor most other tools require and receive more accurate annotations in the process.
Dataloop aims to drive AI to production with end-to-end data management, automation pipelines, and a quality-first data labeling platform. Their video annotation features includes:
- Scene classification
- Object tracking
- Hidden objects
Overall, Dataloop does well at metadata management, however, their ‘AI Tracker’ only works with bounding box annotations. While users can still access video segmentation, the lack of automation can make more complex annotations unworkable.
V7 allows for collaboration and automated workflows, so you can reach human accuracy faster with 10x more training data. V7 offers features similar to Innotescus like
V7’s Auto-Annotate feature manually does what AVA automates, though it still offers fine-grain, manual control of video annotation.
Superannotate aims to power up your video annotation project through cutting-edge annotation tooling, video classification, and interpolation. Important features are:
- Bounding Box Tracking
- Key Point Annotation
One of their most stand-out features is their robust attribute options. Custom metadata is powerful, and SuperAnnotate knows it.
Labelbox is a collaborative data training platform that creates and manages labeled data for machine learning applications.
Labelbox’s UI is quite simple, particularly their timeline and corresponding controls, and their interpolation feature, similar to that on Innotescus and other platforms automates some of the annotating work when users are working with bounding boxes.
However, Labelbox only accepts .mp4 files into their platform, and only their most basic annotation modes have the full scope of video annotation options. When annotating videos with segmentation masks, annotators must step through each frame to view their work – there is no playback option.
(A Conclusion) Ah yes. I remember this… a conclusion, one sec.
As you can see, there is no shortage of computer vision solutions for you and your team to try. Make sure that you make smart software investment decisions and secure the tool that’s best for your needs.
|Automated Segmentation Tracking||No||No||No||No||No|
|Bounding Box Tracking||No||No|
We are a group of scientists, engineers, and entrepreneurs with a vision for better AI. With backgrounds primarily in Machine Learning and Computer Vision, the Innotescus team understands the importance of having full control over and insight into data used to train Machine Learning models.
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