Semantic video intelligence employs algorithms that recognize similarity and context in video content through machine learning (ML) and extract relevant information and insights that can be used for different purposes to make video data easy to search, discover, and retrieve. Video analytics can thus be used to classify videos through topics, themes, people, motion detection, etcetera, to gather intelligence. Video AI technology thus helps in diverse industries that range from marketing and consumer engagement videos to crowd control to security and surveillance.

2. The Practical Guide to
Video Content Analysis

3. Table of Content
● What Is Semantic Video Intelligence?
● Why Is Video AI Critical For Your Business?
● How Is Video Content Analysis (VCA) Done?
● How is Search Inside Video done?
● What Are The Applications Of Video Analytics?
● Why Do We Need Video AI?

4. What Is Semantic Video Intelligence?
Semantic video intelligence employs algorithms that recognize similarity
and context in video content through machine learning (ML) and extract
relevant information and insights that can be used for different purposes to
make video data easy to search, discover, and retrieve. Video analytics can
thus be used to classify videos through topics, themes, people, motion
detection, etcetera, to gather intelligence. Video AI technology thus helps
in diverse industries that range from marketing and consumer engagement
videos to crowd control to security and surveillance.

5. Why Is Video AI Critical For Your
Business?
● Social Media Listening
Businesses can discover insights and gain better ROI on advertising spend through
social media listening on video-based channels like YouTube, TikTok, and even
Facebook, as it aggressively ramps up on user-generated video content.
● Business Intelligence
Through video AI, businesses can extract key information about competitor brands,
consumer trends, marketing challenges, and gain customer feedback.
● User-Generated Content
User-generated content plays a very big part in influencing consumer behavior.
Consumers are always looking for authenticity in product reviews and that’s why
user-generated videos get more views than brand content. Needless to say, video
analytics is needed to dig insights from these videos.

6. How Is Video Content Analysis (VCA) Done?
Video content analysis is conducted in 6 steps. The machine learning solution uses
natural language processing (NLP) including text analysis to extract insights from
video data. These 6 steps are detailed below.
Step 1. Transcription
Step 2. Caption overlay
Step 3. Image recognition
Step 4. Text from post & comments
Step 5. Sentiment & semantic analysis
Step 6. Data Visualization

7. How is Search Inside Video done?

9. What Are The Applications Of Video
Analytics?

11. Why Do We Need Video AI?

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