Opportunities & Challenges

Tagging and indexing large amounts of unstructured video data in real-time and efficiently is a major task, as it is usually performed manually. Unlike traditional rule-based automation processes, AI algorithms can analyse large amounts of data, mine patterns, compare data from multiple sources, and produce intelligent insights.

Efficient indexing and metadata tagging, on the other hand, necessitate sophisticated search techniques aimed at discovering media content snippets. Quality checks, subtitles, and closed caption formation have been traditionally performed manually. Anomaly identification and Natural Language Understanding (NLU) are two tools that AI can use to automate these tasks. Furthermore, AI video analytics can improve the consumer experience by examining consumption habits, social media footprint, demographic data of the local population, and dynamic insertion of highly specific ads, resulting in improved click-through rates.

Appropriate cost functions and hyper-parameter tuning help to fine-tune Deep Learning algorithms. A combination of algorithms such as CNN, RNN, LSTM, NLP, NLU, among others, should be optimized to achieve high accuracy and best performance for specific use cases.