CamSense AI:

Next-generation video recognition

VC Presentation (very cool)

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Problem Statement

Existing video recognition technology doesn’t easily allow custom triggers based on content understanding of unattended recorded video

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Eagle has landed

Flood threshold reached

wildfire precursor, public safety event, etc.

Other video feed (forest, street)

Eagle nest video feed

Local river

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Solution

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• Use inference to gain content understanding of video clips with differences and measure similarity, saving time in data labeling, event identification and response

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• Cluster video clips based on embedding similarities

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• Human review needed to confirm labeling / identification

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Solution wireframe

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Solution explanation

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1. Submit clips (spliced video in 10-sec or other increment)
2. 12 Labs discerns difference between any two video clips, one designated ‘reference’ and all others are used in comparison
1. Output: Annotates the videos.
3. Groq
• Input: Annotations from 12 Labs
• Output: Description of differences using sentence-transformers/all-mpnet-base-v2`
4. If Then Logic:
• Are the annotations similar enough to ignore?
• How? Embedding distance measurement
• If different enough log the clip, timestamp, and description…
5. Aggregate all logs to make a final report

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Funding Ask

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• $1050 in initial seed funding
• Groq sponsorship through stylish gear and $250
• $300 and 1 year of Claude Pro

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Summary and Closing

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