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Computer Vision Systems • Object Detection (R-CNN, YOLO, Single-stage vs Two-stage)Hard⏱️ ~3 min

Video Optimization and Multi Camera Deployment Strategies

Video Optimization Challenges
Running detection on every video frame is computationally expensive. At 30 FPS, you need 30 inferences per second per camera. Multiple cameras multiply this cost. Production systems need optimization strategies that maintain detection quality while reducing compute.
Frame Skipping and Interpolation
Keyframe detection: Run full detection every Nth frame (typically every 3-5 frames). Interpolate bounding boxes for skipped frames using motion vectors or simple linear interpolation. Reduces compute by 60-80% with minimal quality loss for slow-moving objects.
Motion-based triggering: Only run detection when significant motion is detected. Use cheap background subtraction or frame differencing to identify motion regions. Detection runs only on frames with activity.
Multi-Camera Deployment
Centralized inference: Stream video from multiple cameras to a central GPU server. Batch frames from different cameras together for efficient GPU utilization. Requires low-latency network and sufficient bandwidth.
Edge inference: Deploy small models on edge devices co-located with cameras. Only send metadata (detected objects, boxes, confidences) to central system. Reduces bandwidth by 100x+ compared to streaming video.
Hybrid approach: Run fast, lightweight detection at the edge for filtering. Send interesting frames to central server for high-accuracy analysis. Balances latency, bandwidth, and accuracy.
Object Tracking Integration
Tracking assigns consistent IDs to objects across frames. Instead of detecting every frame, detect periodically and track between detections. Tracking is 10-50x faster than detection. The combination provides real-time performance with detection-level accuracy.
💡 Key Insight: Detection answers what is in the frame. Tracking answers where did it go. Production video systems need both, with tracking doing the heavy lifting between detection keyframes.

💡 Key Takeaways

✓Frame skipping with interpolation reduces compute 60-80% with minimal quality loss

✓Motion-based triggering only runs detection when activity is present

✓Edge inference reduces bandwidth 100x+ by sending metadata instead of video streams

✓Tracking is 10-50x faster than detection - use detection periodically, track between

📌 Interview Tips

1Interview Tip: Discuss the detection + tracking pattern - detect every Nth frame, track the rest

2Interview Tip: Mention edge vs centralized trade-off: latency and bandwidth vs model capability

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