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AI & Machine Learning Case Study
Face Detection in Dense Crowds
Research Project: Deep learning system trained to detect and resolve occluded faces in high-density areas.
94.6%detection
< 120mslatency
Resilientocclusion
Project Overview
A specialized computer vision security system designed to detect human faces in high-traffic places like sports stadiums, public transit, and airports.
The Problem
Standard face detection systems experience massive false-negatives when individuals stand close together and overlap.
The Solution
Configured an anchor-optimized convolutional neural network. Applied custom non-maximum suppression thresholds to preserve detections on overlapping faces.
System Architecture
Employs an anchor-optimized deep learning model with feature pyramid networks (FPN) to fuse semantic information across multiple scales, maximizing detection rates of small targets.
[High-Res Video Feed] ==> [PyTorch Anchor FPN Grid] ==> [Non-Maximum Suppression (NMS)] ==> [CUDA GPU Process]
Key Engineering Features
- ◆Dynamic scale estimation for overlapping and tiny facial shapes.
- ◆Optimized for GPU acceleration (CUDA) to process video live streams.
- ◆High reliability under low light and shadow variation.
Technologies Utilized
PythonPyTorchOpenCVTensorFlowCUDACNN
Repository StateOpen Source (MIT)
Deployment LevelStaging / Verified Node