Create Readme.md for gait recognition

Author: 770navyasharma

OpenCV Projects/Gait Recognition/Readme.md

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+# Gait Recognition Project
+## Description
+The Gait Recognition project focuses on recognizing individuals based on their walking patterns. Gait recognition is a biometric authentication technique that identifies people by analyzing the unique way they walk. This technique has a wide range of applications, including security, surveillance, and even healthcare for detecting abnormalities in walking patterns.
+
+This project uses OpenCV for video processing and image extraction, and Machine Learning for classifying the gait patterns of different individuals.
+## Features
+- **Video Processing**: Extract frames from video to analyze walking sequences.
+- **Pose Estimation**: Track key points of the human body to model the walking pattern.
+- **Gait Classification**: Classify individuals based on their walking patterns using machine learning models.
+- **Custom Dataset Support**: Can be adapted to different datasets of gait sequences for training and testing.
+
+## Dependencies
+To run this project, you need the following libraries installed:
+- OpenCV for video and image processing:
+```
+pip install opencv-python
+
+```
+- Numpy for numerical operations:
+```
+pip install numpy
+
+```
+- scikit-learn for training the classification models:
+```
+pip install scikit-learn
+
+```
+- TensorFlow or PyTorch (optional) for deep learning models (if using advanced classification):
+
+```
+pip install tensorflow
+
+```
+or
+```
+pip install torch torchvision
+
+```
+
+## How to Run
+- Install the required dependencies mentioned above.
+- Clone the project repository:
+```
+git clone https://github.com/your-repo/gait-recognition.git
+
+```
+
+- Navigate to the project directory:
+```
+cd gait-recognition
+
+```
+- Prepare the dataset:
+  - Place video files of individuals walking into the data/ folder.
+  - Ensure that the videos are named appropriately for each individual (e.g., person_1.mp4, person_2.mp4).
+- Run the script to extract gait features and classify individuals:
+```
+python gait_recognition.py
+
+```
+## How It Works
+Gait recognition works by extracting frames from a video sequence, detecting the human body in each frame, and then tracking key points such as the head, shoulders, hips, and feet. These key points form a "pose" for each frame, and the sequence of poses over time is used to capture the unique walking pattern (gait) of an individual.
+## Step-by-Step Process:
+- Frame Extraction:
+The video is processed to extract individual frames. Each frame is analyzed to detect the person in the scene.
+- Pose Estimation:
+  - The key points of the human body are detected using a pose estimation model (such as OpenPose or the PoseNet model from TensorFlow).
+  - These key points (like the head, shoulders, and knees) are tracked over time, forming a sequence of body movements.
+- Feature Extraction:
+The relative positions of key body points are extracted from each frame to form a feature vector for each step in the walking cycle.
+
+- Classification:
+Machine learning models (such as Support Vector Machines, Random Forests, or Neural Networks) are used to classify the feature vectors based on the unique walking patterns of different individuals.
+- Prediction:
+Once the model is trained, it can classify the gait of new individuals based on their walking patterns.
+```
+- data/                      # Folder for video data
+- gait_recognition.py         # Main script for gait recognition
+- model/                      # Folder to save trained models
+- README.md                   # Project documentation
+
+```