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Academic & AI researchAI & ML
Face Detection in Dense Crowds
Deep learning system trained to detect and resolve occluded faces in high-density areas.
Headline results
- Latency
- < 120ms
- Detection
- 94.6%
The project in 6 images
5 summary slides · 1 genuine image from the project
01Overview
The project at a glance
A specialized computer vision security system designed to detect human faces in high-traffic places like sports stadiums, public transit, and airports.
- Type
- Academic & AI research
- Category
- AI & ML
Tech stack
- Python
- PyTorch
- OpenCV
- TensorFlow
- CUDA
- CNN
02The challenge
The research question
Standard face detection systems experience massive false-negatives when individuals stand close together and overlap.
The brief
Faces in crowded environments are often partially hidden, pixelated, and shaded, leading to massive detection errors using standard algorithms.
03The solution
The model pipeline
Configured an anchor-optimized convolutional neural network. Applied custom non-maximum suppression thresholds to preserve detections on overlapping faces.
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.
- 01
Data
- OpenCV
- 02
Model
- PyTorch
- TensorFlow
- CNN
- 03
Evaluation
- Latency < 120ms
- Detection 94.6%
- 04
Output
- AI & ML
04Key features
What it does
- 01Dynamic scale estimation for overlapping and tiny facial shapes.
- 02Optimized for GPU acceleration (CUDA) to process video live streams.
- 03High reliability under low light and shadow variation.
05Results
What it achieved
Maintained a high 94.6% detection rate under dense crowd occlusions, with an inference speed under 120ms per frame.
Next step
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