Unleash the full potential of computer vision with professionally annotated datasets by using a video annotation service from Label Your Datacontact us
This kind of video annotation our clients require most of the time. To detect and locate objects in the footage, our video annotation experts use bounding boxes to track objects in the video and generate training data. We also use bounding boxes for action recognition for a detailed activity analysis in the videos.
Keypoint or landmark annotation helps us with picking up on physical characteristics of the body (i.e., facial expressions or body postures and movements). Our annotators connect individual points across the objects to outline them and form variations. Using keypoints, we can produce accurate datasets for CV models aimed at analyzing human movements in the videos.
When we annotate a video file and there are objects that are static by nature yet might change from frame to frame in video datasets, we use polylines. These are short lines joined at vertices that help us define the linear structures and trace the outline and geometry of things like highways, train tracks, or pipelines.
This is the process of drawing three-dimensional boxes over an object in the video. Video annotation for computer vision using 3D cuboids can be applied for a number of industries, including automotive sector and robotics. It also works great for object detection and tracking tasks, especially those where depth perception is crucial.
Security is the top benefit you can ask for when you decide to outsource video annotation services. Oftentimes, we work with CCTV footage containing sensitive data. We’ve had a team of 200 office-based annotators who were annotating CCTV footage in our PCI DSS compliant offices because the videos contained people’s faces, which is PII (Personally Identifiable Information).
Label Your Data supports any kind of data format. If you are not sure how to prepare the video for annotation, you can just give us your dataset, and we will split the videos into a suitable number of frames. Also, there are no requirements for input data: you can send your videos with clarifications of what FPS (frames per second) rate is required, or already cut frames.
Our video annotation company uses an in-house version of the annotation selection tool. This includes the possibility of automated box annotation, which allows saving time on the markup of similar frames. We also offer our annotation software, which can do interpolation, resulting in a shorter turnaround time and more competitive pricing of our video annotation service.
Label Your Data offers a free pilot project to demonstrate our expertise and negotiate key project requirements. All you need is to send us your data and specify all the details. By running a pilot, you can better estimate the runtime, assess the performance of our annotators, the QA process, and improve your project goals.
For successful model performance, our team follows a nuanced methodology that relies on high-caliber video annotation process:
First, we gather the video data. Data collection usually happens on the client’s side. But if you don’t supply any data, our team performs data collection at your request. In this case, you determine the type of data to gather, the volume, and the method for acquiring it.
At this stage, we coordinate with you the key project details. Together, we decide on the process, policies, data labeling criteria, and annotation tools to create a complete training dataset for their CV model. We have a flexible annotation infrastructure, and we can offer our solutions or use your tools.
As we receive the first batch of data, our annotators run a small annotation sample to verify all the edge cases with you. A free pilot helps decide whether our video annotation service can satisfy your demands. Setting realistic quality, timeliness, and productivity goals for the team are essential at this stage.
Once the pilot is done and the results are satisfactory, we proceed to full-scale annotation by assigning a dedicated team to the project. Our annotators work for 8 to 10 hours each day. On request, we can set up on-site teams and provide the option of working in the office. In some cases, we use labeling tools and predictive models to automate simple tasks.
Before sending the completed annotations, we ensure their quality and validity. Our QA specialists and project managers implement a tight quality control. To ensure the number of mistakes is negligible, Label Your Data delivers a thorough QA. We locate data drift and anomalies that may necessitate additional labeling procedures.
Our 10+ years of experience in building remote teams allows us to expertly navigate 500+ data annotators and provide high-quality video annotation services in 55 languages. If you choose to outsource video annotation services to us, you choose the winning mix of quality, speed, and security of your video data.
Complex video annotation process for multiple objects
Using automated methodologies for the labeling process optimization.
For a depth perception technology business developing CV algorithms for car cameras, we provided the detailed and high-quality video annotation of driving footage around rural and urban areas. The project took 50 hours for each area over 6 months. The client requested instance segmentation, so we used automated boxing technology to optimize the work and deliver the finished project on time.
Correct location of key points for different types of bodies
Conducting a comprehensive landmark annotation training for the team.
Label Your Data helped develop a gaming fitness app that combines physical activity with gaming. To make the app work, an algorithm required labeled data to understand how a person moves. We annotated around 500 five-minute video clips of people moving for different exercises. For a more visible motion, we used landmark annotation to place key points on different types of bodies of the athletes.
Deep understanding of human biology
Hiring and training data annotators with a specialized background.
A driver monitoring system company asked us to assist them in developing a smart interior sensing algorithm for increasing driver safety. We were tasked to label 5000 minutes of videos of bodily movements for vehicle integration by using landmark annotation. Annotated data was used to build an anti-sleep algorithm that would issue a warning for a driver and activate certain systems like radio or rear windshield wiper.
Video annotation in computer vision may be laborious. So, hiring a company that specializes in video annotation services for computer vision models, like Label Your Data, could be a smart move.
The labeled video data helps machines recognize or track objects in the video and, thus, makes computer vision algorithms more accurate and reliable.
To train CV models to recognize or identify things in the video, a video annotator assigns the right labels to it by performing a frame-by-frame annotation.
It’s the division of a video stream into discrete groups of related frames. The most popular segmentation techniques divide video into shots, camera-takes, or scenes.