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Cognitive Video Services



Cognitive Video Services




Unlocking new revenue streams using AI 

Operators are shifting focus & also eyeing business opportunities in areas like Smart Home Services, Security & Surveillance Services, Smart Infrastructure Service, etc.

Buying Trends survey shows a massive boost in AI and machine learning adoption in the broadcast and media industry, with 68% of organizations stating they are likely or very likely to deploy AI in

the next 2-3 years. Applications for Digital Asset Management (DAM) is estimated to reach $8.1

billion by the end of 2024.

Opportunities & Challenges

Opportunities & Challenges

Opportunities & Challenges

Real time, efficient tagging and indexing of huge volumes of stored / archived unstructured video data is a major challenge; as this is currently done manually. Unlike conventional automation processes(which are mostly rule-based) AI algorithms can analyze a massive amount of data, mine patterns, correlate data from various sources and can generate intelligent insights.

However, effective indexing and metadata tagging require advanced search techniques, that aim to discover media content snippets. Traditionally quality checks, subtitle and closed caption creation are done manually. AI has the potential to automate these using techniques like Anomaly detection and Natural Language Understanding (NLU). Further, AI can enhance customer experience by analyzing viewing patterns, social media footprint, demographic details of local communities that result in increased click-throughs and also for dynamic insertion of highly relevant advertisements.

Deep Learning algorithms should be fine-tuned using appropriate cost functions and hyper-parameter tuning. Combination of algorithms like CNN, RNN/LSTM, NLP/NLU should be optimized as per the application use cases to obtain optimum accuracy and efficiency.

Service Framework


Core Features

• Image tagging

• Hierarchical object detection

• Named entity recognition such as

• Action detection

• Scene recognition

• Face recognition

• Emotion Recognition

• Strong Language

• Explicit content & Violence


• Automatic highlights generation for


• Talk show Magic Moments creation

Service Features

• Generic data repository

• AI model repository for different


• Speech & text-based query


• Compatible across platforms

• Cloud

• Application Software


  • No dependency on training data collection
  • Generic data repository 
  • Customized inference packages    
  • Requirement specific expert systems    
  • Flexible architecture   
  • Ability to compute at the edge
  • A self-evaluating, continuous learning system
  • NLP/NLU/Context awareness

Benefits to the Customer

  • Reduce time-to-market by up to 50%
  • Algorithm accuracy of over 90% for face detection, eye-gaze and head-pose detection already achieved
  • Easily customizable to suit customer needs
  • 50% Reduction in the time taken to generate Sports Match Highlights 
  • 80% Automation of Highlights & Violence Detection workflow is 80% more effective compared to manual process

Discover More

Automated Content Analysis
Case Study

Automated Content Analysis


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