Apply computer vision and AI to automatically tag, transcribe, moderate, and analyze video content at scale.
AI video analysis includes object and scene detection for auto-tagging, speech-to-text for searchable transcripts, content moderation, highlight detection, and video summarization. AWS Rekognition, Google Video AI, and Azure Video Indexer provide pre-built capabilities.
AI adds value throughout the video lifecycle:
During upload: - Content moderation (block policy violations) - Quality assessment (blur, darkness detection) - Duplicate detection
During processing: - Object and scene tagging - Speech-to-text transcription - Face detection and recognition - Text/logo detection (OCR)
Post-processing: - Highlight and chapter detection - Thumbnail selection - Search index generation - Recommendation signals
The key is integrating AI at the right pipeline stage for your use case, balancing accuracy, cost, and latency.
Computer vision models detect objects, scenes, activities, and concepts in video frames.
How it works: - Extract frames at regular intervals (1-2 fps for cost efficiency) - Run object/scene detection on each frame - Aggregate detections with confidence thresholds - Generate tags with timestamps
Use cases: - Search: Find all videos containing "dog" or "beach" - Organization: Auto-categorize by content type - Recommendations: Similar content discovery - Compliance: Detect restricted content
Provider options: - AWS Rekognition Video: Good accuracy, AWS-native - Google Video AI: Best accuracy, higher cost - Azure Video Indexer: Comprehensive, includes faces - Custom models: Train on your specific content domain
Cost optimization: - Sample frames, don't analyze every frame - Use lower resolution for detection - Cache results, don't re-analyze unchanged content
Modern speech-to-text generates accurate, searchable transcripts across languages.
Capabilities: - Real-time or batch transcription - Multi-language support - Speaker diarization (who said what) - Punctuation and formatting - Custom vocabulary for domain terms
Applications: - Closed captions/subtitles: Accessibility compliance - Search: Full-text search within videos - Translation: Auto-generate multi-language subtitles - Analysis: Topic extraction, sentiment analysis
Provider comparison: - Whisper (OpenAI): Best accuracy, self-hostable - AWS Transcribe: Good accuracy, AWS-native - Google Speech-to-Text: Multi-language strength - AssemblyAI: Developer-friendly API
Best practices: - Always offer human correction interface - Store both raw transcription and corrected version - Use custom vocabulary for industry terms - Consider real-time vs batch based on use case
AI moderation detects policy violations before content goes live.
Detection categories: - Nudity and explicit content - Violence and gore - Hate symbols and gestures - Weapons and dangerous items - Spam and policy violations
Implementation patterns: - Pre-publish gate: Block until review - Confidence thresholds: Auto-approve high confidence safe, flag uncertain - Human review queue: AI triage, human decision - Post-publish monitoring: Catch edge cases
Accuracy considerations: - False positives frustrate legitimate users - False negatives risk platform integrity - Tune thresholds based on risk tolerance - Context matters (news vs entertainment)
Provider options: - AWS Rekognition Content Moderation - Google Cloud Vision SafeSearch - Azure Content Moderator - Specialized providers (Hive, Spectrum Labs)
For UGC platforms, content moderation is essential. Combine automated detection with efficient human review workflows.
AI identifies key moments to create highlight reels, chapter markers, and video summaries.
Techniques: - Scene change detection: Visual transitions - Audio analysis: Applause, music changes, speech patterns - Engagement data: Where viewers rewatch, share, or engage - Content analysis: Action sequences, key dialogues
Applications: - Auto-chapters: YouTube-style chapter markers - Highlight reels: Sports, gaming, events - Preview clips: Trailer generation - Skip intro/recap: Netflix-style navigation
Implementation approach: 1. Detect candidate moments (visual, audio, engagement) 2. Score by importance/interestingness 3. Select top N moments with diversity 4. Generate clips with transitions
Considerations: - Combine multiple signals for best results - Context matters (sports highlights differ from lecture summaries) - Human curation improves quality - A/B test highlight selection algorithms
Architecture patterns for video pipelines that handle thousands of concurrent uploads with reliability and cost efficiency.
Read articleStrategies to reduce transcoding, storage, and CDN costs without sacrificing quality or user experience.
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