Video Annotation Services
Video annotation is an important process in preparing
training datasets for deep learning and machine learning
models in the automotive, gaming, AR/VR development
and many other industries.
How Mindy Support can help companies with video annotation challenges
There are many reasons why video data annotation is a challenging process. First of all, since the object of interest is in motion, this makes the task of labeling objects correctly to get precise outcomes more difficult. Also, we need to keep in mind the huge volumes of video annotation that are usually required. In the example mentioned above, we talked about the number of static images that will need to be annotated in a 60-second video. Now, imagine if you have a video that’s several minutes long. The workload will increase by several orders of magnitude and become very time-consuming, which is why video annotation outsourcing is such an attractive option for many companies. Finally, the large number of events that need to be tracked in the video can overlap. This is challenging for the annotation because it requires a high level of accuracy, up to milliseconds, which is quite difficult and requires the right technical approach
Types of Video Annotation Services We Provide
2D Bounding Boxes
This annotation method involves superimposing a rectangular 2D box over the object of interest in each frame, which helps the system identify the objects in the real world. This method is often used for annotating video for the automotive and security as well as media and entertainment industries.
3D boxes offer the system more insights into objects in the image, specifically the length, width and height. Therefore, it is slightly more accurate than the 2D box method mentioned earlier. It is often used to annotate videos for the automotive sector to give the system an understanding of the traffic situation. In addition, cuboids are also used to create algorithms for the operation of robots and drones since they need to analyze not only the objects themselves and their sizes, but also their placement in three-dimensional space and the distance between them.
Lines and Splines
This annotation method is used to delineate boundaries between one part of an image and another. It is often used in the automotive industry to delineate all of the various road lines. However, it can also be used for annotations where a particular region needs to be annotated as a boundary.
Polygons are very useful for annotating irregularly shaped images that do not fit well into rectangular frames. It detects the exact shape and size of the object and also ensures more precise localization. This type of annotation is used in the automotive sector to annotate all of the objects on the road.
This method involves placing keypoints over the area of interest Precisely detect shape variations for motion tracking, facial landmark detection, and hand gesture recognition. This is often used for things like facial recognition in security systems and also in video games for tracking the movements of characters.
Labeling / Tagging
Data annotations tag or label the objects in the frames. This trains the machine learning system to identify objects in the real world. This is also useful for tracking peoples’ movements in the real world by labeling the sequence of events on which all of these actions were taking place with labels.
Classification / Categorization
This method involves classifying or categorizing certain events in the video. This method is useful if you need your product to identify specific movements or actions. It is often used in the gaming, VR and security industries. Classification can be applied to the entire video and it can describe the quality, usefulness or compliance of the video with the stated message.
Event Tracking does not involve annotating the frames themselves, but the video tracks, localizing and labeling events of interest in time. This method is used for detecting all events of interest. Fragments with events can overlap, and video tracks can be duplicated for annotation, if the project involves a multi-class group of labels.
Let’s Expand with Mindy!
We have a minimum threshold for starting any new project, which is 735 productive man-hours a month (equivalent to 5 graphic annotators working on the task monthly).