Automation is no longer a buzzword; it is turning out to be the essence of every business. AI is on everyone’s mind and their company’s systems, helping to progress every operation in new directions hitherto thought impossible. Businesses are therefore rushing to develop AI models for various applications to remain in line with market trends. They are investing to the tune of US$ 94 Billion in 2021 worldwide in data annotation, the process that helps develop these AI models. You too can join the march towards a more profitable and Smart future with your share of investments in data annotation, and video annotations in particular. 

Why video annotations? It’s because video has been grabbing the attention of consumers, and therefore marketers, since its inception. The growth in digital marketing and platforms like TikTok have pushed video marketing to new heights. The ROI figures are also a tantalizing proposition, with 87% of users saying that it increased their sales. Video also helps with remote operations like training and conferences. 

Thus, when you annotate a video, you bring together the advantages of these two powerful entities, with the process helping you to ultimately develop an AI that suits your every need. This article details how you can develop better AI when you annotate video, and what the future holds for this process and your company. 

What Is Video Annotation?

Data annotation is the process of tagging important elements in a data sample with a predefined label so that a Machine Learning (ML) algorithm can identify it accurately and learn about it against other data elements present in the sample. When this happens with videos, it is called video annotation. The intended target subject/object in a video data sample gets tagged against its background in the number of frames that is necessary to satisfy the duration of identification requirement. 

It is similar to image annotation as each frame of the video gets annotated. It can also incorporate audio and text annotation Since audio and written text can be present in that video. Thus, the techniques applied for those processes, such as classification, segmentation, etc., are applicable here as well. Usually, things like bounding boxes are used to demarcate one element from the others. Due to the complex nature of the process, it is typically outsourced to a professional agency to annotate videos. 

Ways In Which Video Annotation Helps Improve AI

Video annotation adds many benefits to your AI model development efforts. These are not necessarily covered by image annotation due to the continuity factor of videos. 

1. Object Tracking

Motion tracking of an object across numerous frames is the primary reason to use AI with video data. It has multiple applications like monitoring a particular vehicle across a designated stretch of road or for a fixed duration, as the case may be. It also finds use in security systems where, for example, a certain person needs to be tracked for suspicious behavior. 

It also plays a crucial role in mission-critical scenarios when used by the military, like when an enemy aircraft needs to be tracked by a plane’s internal computer for real time dynamic location identification, weapons, locking, and post-launch guidance purposes. 

Here, the intended object gets tagged by an annotator or another algorithm repeatedly in each frame. It is a comparative process as the target object is compared against its background and other objects surrounding it in successive frames to confirm whether it is moving or not. Its velocity can also be determined by measuring the rate of displacement in each frame relative to the background or another marker. 

2. Object Isolation

The term “finding a needle in a haystack” applies when you’re looking for something in a large collection of other objects in a video, or someone in a large group of people. Humans may be able to do this but it certainly takes time. Which is why it is being handed over to AI. A machine can quickly recognize the target object amid others, and even take an action if permitted by a supervisor. But it must first know what it is looking for, which is handled via image and video annotations. 

An ML algorithm is fed with many images of the target object that is annotated to train it. That is then fed the video sample and trained to help it recognize the object while it is in motion or stationary via its labeling in those frames. Characteristics like shape, size, color, texture, etc., come into play here and may be used individually or together to aid the process. 

A more advanced use case is facial recognition, where multiple points on the face are noted and tracked so that a particular person is isolated from many others around them. This is at the heart of surveillance systems that are dependent on AI. 

3. Object Location Identification

Oftentimes, the location of the target object in a video is to be known, whether it’s for tracking purposes or another reason. Location identification finds many applications, such as in the military, where AI can be used to provide the real time location of a target and overlay it on a tactical map in real-time for situational awareness purposes. It must be able to even combine data from multiple sources like GPS, aerial, and ground visuals to improve the accuracy of the localization. 

Annotation of the live video feed, therefore, becomes necessary for locating an object in it. By continuously marking the target object across frames concerning its position against the background and other elements surrounding it, it is possible to train an AI model to do the same in videos. 

4. Activity Tracking

The demand for AI to do more with given data is increasing as companies seek to offload more tasks to it. You are likely to find that mere location, identification, and motion tracking of objects may not suffice your requirements either. For example, you may find that the security of your premises needs augmenting with the ability to not only accurately identify a person and what they are holding in their hand, but also how they are behaving in real-time. This will help you better assess the security threat the person may pose. 

Annotate video that’s coming from the security cameras for activity tracking and you will add a new paradigm to your company’s security. The AI will be able to track not just the person as a whole but also their arms, legs, and head to determine whether their motions could be ascribed to something nefarious or not. This ability can help to preempt a threat before the act can be performed. 

Another field of this ability’s application is sports. Athletes push themselves to beat their competition and are always on the lookout for any advantage they can get. Recent developments in data science have given athletes and their support staff the ability to micromanage their activities and collect associated data. Video-based activity tracking using AI is the new effort towards this, and annotation is necessary to accurately identify the minute motions of every athlete in the footage. 

In this annotation procedure, an algorithm is fed video data with the target object containing multiple tracking points on it during training. The points are then tracked to note the motion of the target object and its various parts against a template. When deviations occur from the set parameters of comparison, the algorithm can flag the activity as anomalous. 

5. Deep Learning / Annotation Automation

AI development is a lengthy process because the underlying algorithms need to be fed multiple samples before they can reach an acceptable level of accuracy. The sample number could be so high that it becomes impractical for human annotation to progress. Automating the annotation process is the solution to this problem, and deep learning is the method used often. Deep learning has many layers of algorithms in a hierarchy that successively trains the next one in line to develop an ML algorithm that can accurately annotate samples by itself. 

However, this can only succeed if the initial stages are fed annotated video provided by human annotators. Then, they also need to verify the annotations done by the algorithm in the initial stages for accuracy. With enough iterations of video annotations, you will have enhanced AI accuracy in whatever application you’re using. 

Conclusion

Video is increasing its importance by the day as a media format that can transform a business at every level. Alongside that, is AI which is doing the same. When you annotate videos, you bring the two transformative technologies together into a seamless and potent force that can future-roof your business, increase its brand reputation in the market, and open up new avenues of operations that you may previously have considered inconceivable. 

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