Video Annotation is the process of adding tags or labels to unlabeled videos. These videos are then used to train machine learning algorithms. The tags or labels can range from a simple bounding box around an object in a video or a full segmentation that labels every pixel of the video.
Video annotation helps to train ML models on a variety of tasks ranging from a simple classification task to detecting objects across multiple frames. It plays a crucial role in training ML algorithms that help autonomous vehicles to track objects such as other vehicles, pedestrians, street lights, traffic signboards, etc…
Video Annotation Approaches
Two common approaches are used for annotating videos:
- Single Image Method
Single image method involves the processing of a video as a collection of single images. The video is first converted to a series of hundreds or thousands of image frames and then annotated image by image and frame by frame by maintaining the sequence and order. The drawback of this approach is when trying to label image by image, the time to complete labeling along with the cost increases. Also, the quality of labeled data will be affected as there are chances of mislabeling the objects and transition states in any given frame compared to the preceding and successive frames.
- Continuous Frame Method
In this approach, the video is considered as 4-dimensional having 3-dimensional entities moving with time and are annotated accordingly. It converts videos as a stream of frames and then annotates thereby maintaining the continuity of information captured.
When determining the best approach for annotating videos, the following key aspects have to be considered:
Entity Persistence
It is a key aspect that helps to determine the quality of the labeled videos that are used to build truly effective ML models. When labeling a video, care should be taken to ensure that the object is recognized as the same and not as a different one when it disappears and reappears after a certain point of time. Entity persistence can be maintained by following a holistic approach when annotating videos rather than a series of single images.
Detecting State Change
Once an object is identified to be in a transitory state, annotators should detect the state changes and also identify the type of transitory state the object is in. When the object is in motion across several frames, identifying the transitory states helps to detect the actual change in the position/state of the object as it appears in an individual frame.
Temporal Tagging
Temporal tagging refers to the ability to note subjective and temporary states of a target object. The main advantage of video annotation is the ability to understand these temporal states that cannot be easily perceived from a single image.
About RightClick.AI
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