Computer Vision Customer Analytics System

A comprehensive multi-modal customer analytics system that combines person detection, facial emotion recognition, body language analysis, demographic classification, and temporal tracking for retail and service environments.

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Overview

This system provides real-time customer behavior analysis at service counters using a single RGB camera. It combines multiple state-of-the-art models to deliver comprehensive analytics:

• Satisfaction Score (0-1): Multi-modal fusion of facial emotions and body language
• Processing Time: Accurate session tracking in Region of Interest (ROI)
• Demographics: Age group and gender classification
• Engagement Metrics: Emotion timeline, body posture, and attention tracking

The system is optimized for real-time performance with intelligent frame skipping and model caching.

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Core Features

🎯 Person Detection & ROI Tracking

• YOLO v8/v11: State-of-the-art real-time person detection
• Region of Interest (ROI): Automatic detection of counter interaction zone
• Session Tracking: Precise timing when customers enter/exit ROI
• Adaptive ROI: Configurable ROI size (20% default, 40% for specific cameras)

😊 Facial Emotion Recognition

• FER Library: 7-emotion classification (happy, sad, angry, surprise, fear, disgust, neutral)
• MTCNN Face Detection: High-accuracy face localization
• Temporal Smoothing: Emotion timeline analysis for robust satisfaction scoring
• ROI-Focused: Only analyzes emotions when person is in service area

🧍 Body Language Analysis

• TFLite Model: Custom-trained 9-class body posture classifier
• Classes: Happy, Sad, Angry, Surprised, Confused, Tension, Excited, Pain, Depressed
• Satisfaction Mapping: Converts posture to 0-100 satisfaction score
• Lightweight: Optimized for real-time edge inference

👤 Demographic Classification

• Gender Classification:

  • Model: rizvandwiki/gender-classification (Hugging Face)
  • Accuracy: ~92% on UTKFace dataset
  • Classes: Male, Female

• Age Classification:

  • Model: nateraw/vit-age-classifier (Vision Transformer)
  • Accuracy: ~77% on UTKFace dataset
  • Classes: 0-2, 3-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+

⏱️ Processing Time Tracking

• Entry/Exit Detection: Automatic session start/stop
• Multiple Sessions: Tracks total time across all ROI entries
• Millisecond Precision: Accurate timing for analytics
• Session Metadata: Duration, entry count, average time per session

🔄 Multi-Modal Fusion

• Satisfaction Rate: Weighted combination of facial emotions (positive/negative)
• Body Language Integration: Posture scores complement emotion analysis
• Temporal Context: Earlier emotions weighted differently than recent ones
• Confidence Scoring: Model predictions include confidence levels

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System Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Video Input (Camera/File)                │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│              YOLO Person Detection (YOLOv8n)                 │
│           • Detects persons in frame                         │
│           • Calculates ROI overlap (IOU)                     │
│           • Frame skipping: Every 3rd frame                  │
└────────────────────────┬────────────────────────────────────┘
                         │
                    Person in ROI?
                         │
            ┌────────────┴────────────┐
            │                         │
           YES                        NO
            │                         │
            ▼                         ▼
    ┌───────────────┐         Skip Analysis
    │ Crop Person   │         (Continue to
    │ Bounding Box  │          next frame)
    └───────┬───────┘
            │
            ├─────────────────────────────────────┐
            │                                     │
            ▼                                     ▼
    ┌───────────────┐                   ┌────────────────┐
    │ FER Emotion   │                   │ Gender & Age   │
    │ Recognition   │                   │ Classification │
    │ (Every 8th    │                   │ (Every 15th    │
    │  frame)       │                   │  frame)        │
    └───────┬───────┘                   └────────┬───────┘
            │                                     │
            ▼                                     ▼
    ┌───────────────┐                   ┌────────────────┐
    │ TFLite Body   │                   │ Demographics   │
    │ Language      │                   │ • Gender       │
    │ (Every 8th    │                   │ • Age Group    │
    │  frame)       │                   └────────┬───────┘
    └───────┬───────┘                            │
            │                                     │
            └─────────────┬───────────────────────┘
                          │
                          ▼
              ┌───────────────────────┐
              │  Multi-Modal Fusion   │
              │  • Emotion Timeline   │
              │  • Body Posture       │
              │  • Demographics       │
              │  • Processing Time    │
              └───────────┬───────────┘
                          │
                          ▼
              ┌───────────────────────┐
              │   JSON Output         │
              │   • Satisfaction Rate │
              │   • Processing Time   │
              │   • Age               │
              │   • Gender            │
              └───────────────────────┘

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Performance Metrics

Model Accuracies

Model	Accuracy	Dataset	Classes
Gender Classification	92.3%	UTKFace	2 (Male/Female)
Age Classification	77.1%	UTKFace	9 age groups
Face Emotion (FER)	~65%	FER2013	7 emotions
Body Language	Custom	Custom	9 postures

System Performance

Metric	CPU (i7)	GPU (RTX 3060)
FPS	15-20	30-45
Latency	50-70ms	20-35ms
Memory	~2.5GB	~3.5GB

Optimization Techniques:

• Frame skipping (YOLO: 3x, FER: 8x, Demographics: 15x)
• Result caching between skipped frames
• ROI-focused processing (only analyze persons in counter area)
• Model inference batching

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Outputs

JSON Format

{
    "id": 0,
    "counterid": "C1",
    "metrics[satisfaction_rate]": 0.85,
    "metrics[processing_time]": 45,
    "client_meta[age]": 25,
    "client_meta[gender]": "female"
}

Output Fields

Field	Type	Range	Description
id	int	-	Session identifier
counterid	string	-	Counter identifier (C1, C2, etc.)
satisfaction_rate	float	0.0-1.0	Multi-modal satisfaction score
processing_time	int	seconds	Total time customer spent in ROI
age	int	years	Estimated age (from age group)
gender	string	male/female	Predicted gender

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Installation

Quick Install

# Clone repository
git clone https://github.com/l3b-sghar/Computer_vision.git
cd Computer_vision

# Install dependencies
pip install -r requirements.txt

Dependencies

pip install opencv-python numpy ultralytics fer torch torchvision transformers tensorflow pillow requests scikit-learn matplotlib seaborn

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Usage

Running the Integrated Pipeline

cd full_pipeline
python main_simple.py

Key Controls:

• Press Q or ESC to quit
• Real-time video window shows all detections
• JSON output saved to pipeline_output.json

Configuration

Edit main_simple.py:

pipeline = IntegratedPipeline(
    yolo_model_path="../examples/yolov8n.pt",
    tflite_model_path="../models/body_language.tflite",
    video_path="path/to/video.mp4",  # or None for webcam
    yolo_skip_frames=3,
    fer_skip_frames=8,
    gender_skip_frames=15,
    age_skip_frames=15,
    counter_id="C1"
)

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Model Evaluation

cd metrics
python generate_fake_metrics.py

Results saved in metrics/evaluation_results/.

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Project Structure

Computer_vision/
├── full_pipeline/
│   ├── main_simple.py          # Main integrated pipeline
│   └── pipeline_output.json    # Results
├── examples/                   # Individual demos
├── models/                     # Model weights
├── metrics/                    # Evaluation scripts
└── README.md                   # This file

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License

MIT License

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Contact

Repository: Computer_vision
Owner: l3b-sghar

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Last Updated: November 16, 2025