BLOG

How GenAI is Revolutionizing Video Understanding and Analysis

A detailed guide to using GenAI in the process of video making, understanding, and analyzing.

Author : Dhanya Bibin

Date: 02 February 2024

In today’s fast-paced digital world, consuming vast amounts of video content can be time-consuming and overwhelming. Whether you’re a content creator, a student, or a professional seeking key insight, the sheer volume of information contained in lengthy videos can pose a challenge.

Enter GenAI, the next-generation artificial intelligence revolutionizing the way we interact with video content.

Understanding GenAI

GenAI, short for generative artificial intelligence, refers to a class of artificial intelligence systems that has the ability to generate new content, whether it’s image, text, audio, or other forms of data. These systems are designed to understand patterns and structures within the input data and use that understanding to produce new similar content.

GenAI represents a groundbreaking leap in the field of video summarization. GenAI is designed to distill extensive video content into concise, meaningful summaries by leveraging the power of advanced deep learning algorithms. This innovative technology has the potential to transform the way we engage with videos, making information extraction more efficient and accessible.

How Video Summarization Using GenAI Works

Using GenAI in video summarization involves leveraging models that can understand and generate meaningful content from video data. To incorporate Gen AI into video summarization, the first step is to define the objective of your summarization.

Are you looking for keyframes, important segments, or a textural summary? Also, you need to consider the context of your application (security surveillance, video content creation, etc.). Then, depending on the specific requirement, choose a generative AI model suitable for the context of the application.

It can be a pre-trained model like GPT-3, Llama 2, BART, or Bloom, or training your own model using frameworks such as TensorFlow or Py torch. The next step is the preparation of the video data by converting it to a format suitable for the chosen generative AI model. Next is the extraction of key points, which helps the model to understand the content of the video, looking for important actions, words, and emotions. The most important step is the summarization step, where you use the generative AI model to generate summaries of the entire video. After summarization, you need to refine and organize the summaries. It involves filtering out redundant information or prioritizing certain types of content based on the objectives. Evaluation of the generated summaries can be performed by comparing the generated summaries with ground truth summaries using specific metrics. After evaluation, if the quality of the summary is not satisfactory, fine-tuning of the model can be considered.

How Video Summarization Transforms OTT Experiences

The various steps of video summarization, as mentioned in the previous section, lay the foundation for a transformative impact on the OTT industry. Here, we’ll explore how this connects with the needs of both viewers and platforms.

For viewers

Content preview : Video Summarization can generate concise previews or trailers for longer video content. This helps the viewers quickly understand the content and decide if they want to watch the full video.
Personalized recommendations : Summarization can analyze user preferences and generate personalized summaries. This helps to deliver content based on the user’s interests.
Improved Accessibility: Generate audio summaries or caption summaries for viewers with disabilities to make content more inclusive.
Enhanced learning: Create summaries of educational videos and documentaries to make learning easier.

For Platforms

Content discovery: Make the content more discoverable by indexing video summaries for search and browsing, which improves user experience.
Engagement boost: Using summaries for targeted promotions and trailers helps viewers watch specific content and increases watch time.
Personalized summaries: Generate personalized summaries for different user segments, which helps tailor content recommendations.
Content analysis and insights: Analyse video summaries to understand user preferences and trends, which helps in content acquisition and production.
Cost reduction: Automation of content summary generation tasks helps save time and resources compared to the manual process of summary generation.

Challenges

Like any new technology, GenAI-based video summarization also comes with its own challenges.

Bias: GenAI models are trained on large amounts of data, and that data can be biased. You need to be aware of potential biases and take steps to mitigate them.

Accuracy: Video Summarization is a complex task, and GenAI-based models can sometimes make mistakes. It’s important to evaluate the generated summaries carefully and ensure they accurately reflect the original video.

Transparency: Understanding how GenAI models generate summaries is crucial for building trust and ensuring responsible use of the technology.

Conclusion

Despite these challenges, the future of GenAI-based video summarization is bright. As these models are getting better and better, they have the potential to change the way we consume and interact with video content.

Imagine finding the perfect video instantly, understanding a whole lecture in minutes, or even watching videos in other languages with instant summaries! The possibilities are amazing.

At Logituit, we are working towards immense possibilities. We built Logix Enrich ahead of its time with the powerful implementation of GenAI. It has already set standard across the OTT platforms through its advanced capabilities such as object detection, highlight generation, scene analysis, intelligent ad placement, emotion analysis and so on.

To learn more, write to us at [email protected]

About Author

Dhanya Bibin

Dhanya specializes in developing innovative solutions utilizing generative AI and deep learning. Her focus lies in the dynamic domain of video streaming and OTT, where she leverages cutting-edge technologies to enhance user experiences. With a keen interest in exploring the intersection of artificial intelligence and real-world applications, she’s dedicated to pushing the boundaries of what’s possible in this dynamic field.

Beyond her tech pursuits, she is an avid gardener, cultivating a cherished collection of rare orchid plants. Her passion for orchids seamlessly intertwines with her curiosity about AI’s real-world applications.