Real-time Object and Emotion Recognition

Project Overview

This advanced computer vision project combines real-time object detection and emotion recognition using an ESP32-CAM module. The system streams video wirelessly and processes it using state-of-the-art AI models to identify objects and detect human emotions simultaneously.

Key Features

• Dual AI Models: YOLO for object detection + DeepFace for emotion recognition
• Real-Time Processing: Live video stream analysis with minimal latency
• Wireless Streaming: ESP32-CAM transmits video over Wi-Fi using TCP
• Automated Setup: Bash scripts for multi-service startup
• Environment Management: Conda environments for dependency isolation

Technical Architecture

Hardware

• ESP32 module with Camera module
• Wi-Fi connectivity for video streaming

Software Stack

• Computer Vision: OpenCV for frame processing
• Object Detection: YOLO (You Only Look Once) model
• Emotion Recognition: DeepFace library
• Networking: TCP protocol for video transmission
• Automation: Bash scripts for service orchestration

Processing Pipeline

1. Capture: ESP32-CAM captures video frames
2. Transmission: Frames sent via TCP over Wi-Fi
3. Reception: Python server receives video stream
4. Preprocessing: OpenCV processes and prepares frames
5. Detection: YOLO identifies objects in frame
6. Recognition: DeepFace analyzes facial emotions
7. Rendering: Annotated output displayed in real-time

Applications

• Security and surveillance systems
• Human-computer interaction
• Retail analytics
• Smart home automation
• Accessibility tools

Performance

• Real-time processing at 15-20 FPS
• Simultaneous multi-object and emotion detection
• Low-latency wireless transmission