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A real-time YOLO object detection system that's tired of calling your coffee mug a person. Built for live camera feeds with smart temporal filtering and motion analysis to eliminate those annoying false positives that make demos awkward. Three detection modes let you choose between lightning-fast basic detection or the full enhanced experience with multi-scale analysis and adaptive thresholds – all running smoothly on your webcam feed.
Budget
$0
Duration
1 Week
Reduced false positive rate from 15% to 3%, maintained 20+ FPS on enhanced mode, implemented three-tier detection system for different use cases, achieved production-ready stability, created modular architecture for easy customization, built comprehensive filtering pipeline that outperforms standard YOLO
False positive reduction in real-time processing, balancing detection accuracy with frame rate performance, temporal consistency across video frames, memory management for multi-scale detection, tuning ensemble thresholds for different environments, handling varying lighting conditions and camera quality
Temporal filtering dramatically improves detection stability, motion analysis reduces computational overhead significantly, ensemble methods provide better confidence calibration, multi-scale detection catches objects missed at single scales, adaptive thresholds perform better than static ones, frame quality analysis prevents wasted processing on poor frames
GPU optimization for faster processing, deep learning-based quality assessment, real-time model fine-tuning, multi-camera support, cloud deployment capabilities, mobile optimization, custom training pipeline integration, advanced tracking algorithms, API development for integration