Temporal Downsampling and Motion Gating for Cost Efficiency

Temporal Downsampling
Not every frame needs analysis. Consecutive video frames are highly similar. Running detection on every frame wastes 90%+ of compute on redundant analysis. Smart frame selection reduces cost without sacrificing detection quality.
Fixed interval sampling: Analyze every Nth frame (e.g., every 5th frame). Simple but misses fast events. A person walking through the frame in 3 frames gets detected; a falling object in 2 frames might be missed.
Adaptive sampling: Analyze more frequently during activity, less during quiet periods. Activity level determines sampling rate dynamically.
Motion Gating
Only run expensive ML models when motion is present. A parking lot at 3 AM sees no activity for hours. Running detection continuously wastes resources.
Two-stage approach: Run cheap motion detection (frame differencing, background subtraction) on every frame. Only trigger ML inference when motion exceeds threshold. Reduces ML inference by 80-95% during quiet periods.
Motion region cropping: When motion occurs, only analyze the active region rather than the full frame. A person in the corner of a 4K frame can be cropped to 640x480 for faster inference.
Cost Impact
Combining temporal downsampling and motion gating typically reduces GPU costs by 5-10x compared to naive every-frame processing. A system that would need 100 GPUs might run on 10-20 GPUs with smart filtering.
💡 Key Insight: The goal is not to process every frame. The goal is to detect every event. Smart filtering achieves the same detection outcomes with 90% less compute.

💡 Key Takeaways

✓Temporal downsampling analyzes every Nth frame - 90%+ of consecutive frames are redundant

✓Motion gating runs cheap detection first, triggers ML only when activity present - 80-95% reduction

✓Combined filtering reduces GPU costs 5-10x vs naive every-frame processing

✓Goal is detecting every event, not processing every frame - smart filtering achieves both

📌 Interview Tips

1Interview Tip: Frame cost reduction in terms of events detected, not frames analyzed

2Interview Tip: Mention two-stage architecture - cheap filter triggers expensive analysis

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