Video annotation is based on the concept of image annotation. For video annotation, attributes are manually labeled on every video frame (image) to train a machine learning model for video identification. Hence, the dataset for a video identification model is comprised of images for the separate video frames.
Video annotation for machine learning involves segregation of the said video into frames and taking all of these frames with various techniques. The exact number of frames that will need to be annotated will depend on the length of the video and frames per second (fps). For example, you may have a video clip that’s only 60 seconds long, but if the frame rate is 60 fps, that’s 3,600 static images that need to be annotated. As you can imagine, this is a very time-consuming process which is why a lot of companies outsource such work to a video annotation company.
There are many reasons why video data annotation is a tricky process. First of all, since the object of interest is in motion, this makes the task of labeling the objects correctly to get accurate 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 tricky for the annotation because it requires a high level of accuracy, up to milliseconds, which is quite difficult and breath-taking & requires the right technical approach.
When professional experts use automation tools for video annotation, it reduces the chance for any mistake by offering greater continuity across frames. When annotating several images, it’s important to use the same labels for the same objects, but consistency errors are possible. When annotating video, a computer can automatically track one object across frames, and use circumstances to remember that object throughout the video. This provides greater uniformity and accuracy than image annotation, leading to greater accuracy in your AI model’s predictions.
2D bounding boxes
Rectangular boxes are primarily used for object identification, labeling, and categorization in the model. Boxes are manually drawn around the object of interest during motion across numerous frames. The box is placed close to every edge of the object, as is possible, and then labeled as per its class and characteristics.
3D bounding boxes
To achieve a more realistic 3D depiction of a specific item and how it relates to its environment, the 3D bounding box method is applied. This method can indicate the breadth, length, and even the depth of an object which is in motion. This method can be used to detect common as well as particular classes of objects.