How Computer Vision will become a most transformative adaptation of AI

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Since the 1950s, computer scientists have dreamed of a future of computer vision (CV) that enables computers to “see” images much as humans do, with the ability to decipher rich details as extensive as mood and emotional responses. For humans, translating and communicating the context, description, and sense of visual imagery starts in early infancy. For computers, the ability to “see” images is now reaching early maturity.

Advances in computer vision technology are unlocking a wave of innovation and efficiency across a myriad of industries

First, what is Computer Vision?

Since the 1950s, computer scientists have dreamed of a future that enables computers to “see” images much as humans do, with the ability to decipher rich details as extensive as mood and emotional responses. For humans, translating and communicating the context, description, and sense of visual imagery starts in early infancy. For computers, the ability to “see” images is now reaching early maturity.

Computer Vision (CV) is a grandchild of Artificial Intelligence and Machine Learning that enables computers to process, analyze, and make meaningful sense of visual data (images or videos) in the same way humans do, and at a much faster rate. CV is based on training computers to analyze an image or video at a pixel level and understand it. Enabled by machine learning and artificial intelligence, computers retrieve visual information, decipher it, and translate the results back through sophisticated algorithms.

Until recently, CV had interesting applications but many limitations. CV technology has increasingly been able to solve the following issues at scale:

• Object classification. What is located within an image, e.g. is this a picture of a cat?
• Object identification. Is a specific object recognized within an image, e.g. is this the same cat as similar images?
• Object tracking. Within a video, how is the object (or objects) tracking, e.g. where is this cat moving frame by frame through the video?
• Contextual relevance. What is the context of the scene? And more broadly: what is the contextual relationship of this content to other content? Could the activity of the cat be aligned with “chewing?”

While these breakthrough developments led to a vast range of applications, the depth of information extracted only scratched the surface compared to humans’ ability to understand the context from images and videos. Is the cat jumping? Is the cat being pursued? What is the mood or attitude of the cat? Previous CV technology could not provide this granular level of detail.

Why does CV matter?

Today, the volume of images and videos that are created and humans are exposed to is overwhelming and uncontrolled. As a society, we have opened the floodgates to an abyss of video and image content that can be captured, shared, and viewed. For example, understanding the contextual relevance of a brand’s creative ad content to other content is critical to the success of the Internet’s ad-sponsored model. Improved contextual relevance is critical for the media industry to face down privacy issues both in the US and overseas. The ability to scale contextual relevance is also critical as the volume of video increases. Simply consider that the number of hours of video watched on the social media channel YouTube in 2021 was 1 billion hours daily. As we continually rely on and benefit from the exploding video technology, we also need solutions to help prioritize and classify content emerging from this evolving technological shift so both humans and machines can better comprehend it.

What can Netra’s CV technology deliver?

Video, in particular, has been a challenging problem to solve. Simply learning that a video contains a cat and the video might involve chewing is helpful and has its practical uses. But for a broader range of applications, a greater depth of context is needed to truly harness the power of AI and CV. With Netra, our “video x-ray” technology can dive into the following depths:

• Scene classification. Classify content into Subject, Activity, Object, and Place to build a narrative of the video
• IAB Contextual classification. Detect one or many of the 650+ IAB categories for classifying against standard industry taxonomy
• Emotion detection. Understand the emotional response from a video by understanding the detected emotions and resulting viewer response within video content.
• Safety analysis. Detect unwanted video elements and automatically shield viewers from sensitive content (including nudity, hate, disasters, and diseases)
• Anomaly detection. Track activities from street cameras that are out of the ordinary for further human review
• High-interest object detection. Determine time-on-screen, product and prominence placement for brand awareness and other surveillance insights within video.

Netra’s API enables partners to grasp new depth into video at scale and opens the doors to unprecedented commercial applications of CV:

• Unparalleled contextual positioning. Relaying information to advertisers so they can make ad placement decisions through stronger, privacy-safe contextual relevance and matching of ad campaigns to the correct content context to drive advertisers’ ROAS and publishers’ CPM.
• Enhanced brand safety. Classification of user-generated content such as TikTok so advertisers know they are buying ads against brand-safe content,
• Anomaly detection for operational cost savings.  Detecting unexpected behavior that warrants a human need to intervene, e.g. video monitoring so farmers can detect dying crops or security firms can detect suspicious behavior  
• Augmenting information in the platform with video and image intelligence
  Total comprehension and ability to search for ‘more content like this content’ to augment data platforms with visual intelligence information.

Why is Netra’s ability to solve CV unique?

If “a picture is worth a 1,000 words” then a 30-second video is estimated to contain the equivalent of 720,000 words. In the past, processing this amount of granular-level information at scale has been widely unachievable, constraining AI’s ability to translate the depth and richness of insights within video. Netra’s solution, spun out of MIT and now established through four patents, uses AI and machine learning to eliminate the need to scan every frame within a video. This allows for a high-speed, highly-sophisticated synthesis of the depth of information that can be captured within a video. It is also far less costly than other solutions because Netra’s API stores 7-10x less information to deliver the same depth of knowledge compared to our competitors. Further, by leveraging the Netra IP, our partners can run deeper, more thorough classifications of content to deliver higher fidelity classification as well as apply custom active learning tools to meet unique use cases.

How is Netra leading?

Today, most AI tools rely heavily on scanning text to try to decipher moods, emotions, safety, and anomalies. In today’s world, a texted-based approach is no longer the entire picture. The world is rapidly evolving from a text-based form of communication to a video-immersed experience. Increasingly, we do not communicate through letters, text, and emails. We communicate through sound, video, and images. As communication platforms evolve, Netra’s technology aims to help our partners harness the vast complexity of video information into their business objectives, alongside text and image classification into what we call Total Comprehension.

We’re revolutionizing video analysis, and our technology effectively captures, processes, and shares the video elements required to solve your business challenges. Our patented, flexible API is designed to make the complexity of video analysis, accessible, and available for a broadening array of your critical applications.

How can I learn more?

To learn more about Netra or see a demonstration of the platform and how total comprehension can benefit your business, click here or email sales@netra.io

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