Smart Behavioral Video Compression

Sentio Mind · POC Assignment · Project 2

GitHub: https://github.com/Sentiodirector/Assignement_Video_compression.git Branch: FirstName_LastName_RollNumber

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Why This Exists

Four cameras running all day in a school building produce 40 to 80 GB of raw footage. Uploading that to the Sentio Mind server over a typical school internet connection takes 6 to 12 hours. That is not practical.

Blindly compressing with ffmpeg throws away frames that contain people, which breaks the analysis. Your job is to build a smarter compressor — one that keeps every frame containing a human and aggressively discards empty hallway footage and near-duplicate frames.

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What You Receive

p2_video_compression/
├── video_sample_1.mov          ← 2-3 min raw CCTV clip, download from dataset link
├── video_compression.py        ← your template — copy to solution.py
├── video_compression.json      ← schema for segments_kept.json
└── README.md

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What You Must Build

Run python solution.py → it must produce:

1. compressed_output.mp4 — H.264, 12 fps, at least 70% smaller than input
2. compression_report.html — size comparison, duration comparison, thumbnail storyboard
3. segments_kept.json — follows video_compression.json schema exactly

Decision Algorithm (implement in this exact order)

For each frame:

Step 1 — pHash similarity
  Compute perceptual hash of this frame.
  If similarity to last kept frame > 0.95 → discard (near-duplicate).

Step 2 — Motion score
  Compute dense optical flow vs previous frame.
  If motion_score < 0.05 → mark as discard candidate (static empty scene).

Step 3 — Face override
  Run Haar face detection.
  If any face found → keep this frame regardless of steps 1 and 2.

Step 4 — Motion override
  If no face found but motion_score > 0.15 → keep anyway.

Step 5 — Context frame rule
  Every 3 seconds of original video → force-keep one frame no matter what.

Then re-encode all kept frames to H.264 MP4 at 12 fps using ffmpeg.

Performance Targets

• File size reduction: 70% or more
• Processing speed: 2-minute video must finish in 10 seconds or less on a laptop

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Hard Rules

• Do not rename functions in video_compression.py
• Do not change key names in video_compression.json
• Output video must play in VLC without codec issues
• compression_report.html must work offline
• Python 3.9+, no Jupyter notebooks
• ffmpeg must be installed: sudo apt install ffmpeg

Libraries

opencv-python==4.9.0   numpy==1.26.4   imagehash==4.3.1   Pillow==10.3.0

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Submit

#	File	What
1	solution.py	Working script
2	compressed_output.mp4	Compressed video
3	compression_report.html	Report with storyboard
4	segments_kept.json	Segment log matching schema
5	demo.mp4	Screen recording under 2 min

Push to your branch only. Do not touch main.

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Bonus

Auto-calibrate the motion threshold from the first 30 seconds of the video. Different cameras at different lighting levels need different thresholds — hardcoding 0.05 for every camera is fragile.

Sentio Mind · 2026