Video annotation is quite difficult to compare to image annotation. Due to moving objects in visuals, in video annotation, frame-by-frame images are annotation making it difficult for annotators to maintain accuracy. However, with help of certain and techniques video annotation task is achieved making it easier for the machines to detect and track the moving objects in the videos.

   

In machine learning, content in the video can provide rich information for the AI model to learn from the intricate process of annotating these datasets that also reflects the level of complexities. There are only two ways to process the image annotation techniques. 

The first is the Single Image Method, which processes video as a collection of single images. While the second technique called the Continuous Frame Method helps to understand videos as 4 dimensional, with 3-dimensional entities moving through time, and annotates them dimensionally.

In this video, the annotation technique was earlier more used when the automation tools are not available for the annotation. In this method the all the frames from the video is extracted and annotated individually using the standard image annotation technique.

In a 30fps video, there could be 1800 frames per minute that require lots of time and efforts making the entire annotation process costly if there are a huge amount of datasets. And there are high chances of errors as, one object classified as one thing in one frame and another in the next frame.

While on the other hand, thanks to useful autonomous tools, video annotation is not only easy but also faster and more accurate. Yes, in the continuous frame method, videos as a stream of frames, preserving the continuity and integrity of the flow of information captured.    

Computers can automatically track objects and their locations frame-by-frame relying on continuous frame techniques like optical flow to analyze the pixels in the previous and next frames and predict the motion of the pixels in the current frame.

Using this level of context, computers can precisely identify the object that shown at the starting of the video, disappear for several frames and then returns later. While if the annotators use the single image method instead, they might misidentify that object as a different object when it reappears in the frame.      

   

However, compared to image video has its advantage of the ability to represent the transitory state of each instance of an object or person. This enables the same entity can be recognized even if it goes in and out of view. While, conversely reducing a video down to a series of images, generates a duplication of efforts at the time of labelling and identifying objects that remain constant.

Anolytics is the leading data annotation company providing video annotation services for machine learning and AI projects. With expertise in image annotation, text annotation and video annotation, Anolytics can produce a huge quantity of high-quality training data for computer vision AI models.