🤖 Face Direction & Speed Tracker using OpenCV

🧠 Project by: Shema Leandre

Field: Intelligent Systems & Robotics
Date: 2025
Repository: Face_Direction_Tracker_Project

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🎯 Objective

This project demonstrates how to use OpenCV to detect a human face in real time, track its movement direction (Left, Right), and calculate the movement speed in pixels per second.

The system visually overlays:

• A green bounding box around the detected face
• Direction text (Left, Right, Up, Down)
• Speed (px/s) on the live video feed

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🧩 Features

✅ Real-time face detection via OpenCV Haar Cascade
✅ Dynamic direction tracking between consecutive frames
✅ Speed estimation based on pixel displacement per second
✅ Overlay visualization (text + bounding box)
✅ Well-documented, readable, and modular code structure

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🧠 How It Works (Logic Flow)

Step	Operation	Description
1️⃣	Frame Capture	Webcam feed captures live video frames.
2️⃣	Preprocessing	Each frame converted to grayscale for faster detection.
3️⃣	Face Detection	Haar Cascade locates the face rectangle (x, y, w, h).
4️⃣	Center Calculation	Center = (x + w/2, y + h/2) gives face midpoint.
5️⃣	Tracking	Compare center positions between current & previous frame.
6️⃣	Direction Decision	Based on displacement (dx, dy): determines Left/Right/Up/Down.
7️⃣	Speed Computation	Uses Euclidean distance & time delta → speed = distance / Δt.
8️⃣	Display Overlay	Direction and speed are displayed on live video.

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⚙️ Technologies Used

Tool	Purpose
Python 3.x	Core programming language
OpenCV (cv2)	Image & video processing
time module	Measuring frame intervals for speed
Git & GitHub	Version control and collaboration

🧩 Step 1 — Install Dependencies

Make sure you have Python 3.x and OpenCV installed:

1.pip install opencv-python 2.Step 2 — Run the Script 3.Execute the main script in your terminal: python face_direction_tracker.py ❌ Step 4 — Exit To close the live webcam window, press:

🧠 Working Logic (Step-by-Step):

1.Capture Frame: The webcam captures each video frame in real-time. 2.Convert to Grayscale: Makes detection faster and easier for OpenCV. 3.Face Detection: Haar Cascade identifies the bounding box of your face. 4.Calculate Center: Compute (cx, cy) — the midpoint of the face box. 5.Compare Frames: Measure how much the center moved since the last frame. 6.Compute Direction:

If cx₂ > cx₁ → Face moved Right

If cx₂ < cx₁ → Face moved Left

If cy₂ > cy₁ → Face moved Down

If cy₂ < cy₁ → Face moved Up

7.Compute Speed: Calculate pixels moved per second.

8.Overlay Results: Display direction and speed text on the live feed.

🧮 Formula for Speed Speed

( 𝑑 𝑥 ) 2 + ( 𝑑 𝑦 ) 2 Δ 𝑡 Speed= Δt (dx) 2 +(dy) 2

Where:

dx = cx₂ - cx₁ → horizontal movement

dy = cy₂ - cy₁ → vertical movement

Δt → time difference between frames

📊 Example Output When you move your face, the terminal and live video display: Direction: Left Speed: 120.54 px/s ✅ A green rectangle surrounds your face and updates continuously.

📈 Visualization Overview Frame Detected Face Computed Center Output Frame 1 Yes (320, 240) — Frame 2 Yes (380, 240) Direction: Right, Speed: 60 px/s Frame 3 Yes (380, 280) Direction: Down, Speed: 40 px/s

🧠 What I Learned:

From this project, I developed practical understanding of: 🧩 How OpenCV detects and tracks moving objects 🧮 How to measure position change for motion detection ⏱️ How to calculate speed based on time intervals 🧠 The concept of direction vectors in computer vision

🚀 Future Improvements: Improvement and Description 🔹 Use Mediapipe or Dlib For more accurate face landmarks 🔹 Add 3D Head Pose Estimation Estimate angles (pitch, yaw, roll) 🔹 Integrate with servo motors Make a robot head follow user movement 🔹 Data Logging Save motion data for ML training or analytics 🔹 Optimize frame rate Use multi-threading for smoother tracking

📘 References & Learning Materials: 1.OpenCV Official Documentation 2.PyImageSearch Tutorials 3.Real-Time Object Tracking (LearnOpenCV)

🤖 Face Direction & Speed Tracker using OpenCV

👨‍🎓 By: Shema Leandre

Field: Intelligent Systems & Robotics
Year: 2025

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