Tracing newer and improved video content paths in the age of digital entertainment
Everyone loves the entertainment industry because it provides the content to almost every audience. Take the example of videos to calm your pets down. They are found in this industry. They are found in this industry. Despite receiving all this love, the video content scene is moving forward at a different pace than other industries. Yes, but it could be better.
Given that video content is slowly becoming the main marketing means for businesses, one would expect industry veterans to rally behind video producers, agencies, and internal creative teams to deliver the industry delivering high-quality content at a lower cost and with better searchability. In an industry dominated by video content giants like YouTube, we can only boast camera technology advancements, super-fast networks, increased storage, and higher bandwidth availability. The gaming industry is making leaps we can only dream of.
How has Video Content Stagnated?
It’s common knowledge that when an organization monopolizes or dominates an industry, the industry grows old, lazy, and boring. These monopolies have the industry stuck in a time warp with famous content makers becoming lazy. The so-called ‘industry leaders still need to innovate on the business’s content, hardware, and software side, alienating potential young consumers who crave something newer than just another platform for typical videos.
Google, Bing, and Yahoo search engine index pages’ textual content. These search engines have two major functions: crawling and building an index and providing search users with a ranked list of the websites they’ve determined are the most relevant. However, when we dive deeper into understanding video content, the existing search engines need more ability to interpret and rank videos on a page. This results in video content being ‘opaque,’ which means it gets difficult to understand or explain since the existing video metadata is limited and misleading. Moreover, it is uncertain whether metadata accessible to a search engine applies to specific scenes or the video. This is due to the need for indexes at the scene level, which describes the content in temporal terms, with timecode references for each categorization.
What’s the Need for these Improved Search Parameters?
Deep search is not available in videos. You have to watch a long video with the speaker covering multiple topics, but you are only interested in two topics. You can’t navigate these two topics. This makes videos opaque, and viewers may only watch them after the interesting topics. Improving the search parameters means a viewer can navigate to the desired scene in the timeline.
The ability to index and search the information within a particular video beyond its metadata tags provides new avenues for interpreting this content, just like written content. Improved search parameters mean that platforms will witness an increased demand for video organization and retrieval since viewers can now access more useful and straightforward video content.
The AIWORK project has already laid out a working blueprint for achieving this.
How AIWORK is Leveraging Blockchain Technology to Steer the Stagnant Sector Forward
We have multiple technologies that could transform video content if organizations put them to good use. They include artificial intelligence (AI), Blockchain, Virtual Reality (VR), Machine Learning (ML), and Augmented Reality (AR), among others. The AIWORK project realized that to improve the video content industry, they could start by merging AI technology with what Blockchain offers and work upward from there.
This idea works since, as AIWORK explains, what is needed for working with the opaque content of the video is the application of AI computer vision, such as facial recognition, to video indexing. Once the AI understands what a face is, a human can further guide the AI by teaching it to recognize specific faces to help it associate different characteristics and details of each face with a specific tag, such as balding or a person’s name.
Once a face dataset is built, the AI can compare video images with this dataset and identify specific faces, such as a popular celebrity or a known criminal. This same method can recognize objects like a vehicle’s tire, landmarks such as the Eiffel Tower, and action scenes such as a woman parachuting.
To sum it up, videos are a medium for gaining knowledge, learning new skills, and offering entertainment to the masses. People use video searches to look at life from a new perspective; therefore, by using AI and Blockchain technologies to revamp this particular feature, there will be no limit to what viewers can learn by conducting a fast video search.
More on the AIWORK project here:-
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