Gait Detection using Video Streamlit App in Python Projects

Gait Detection using Video Streamlit App in Python Projects
Abstract
Gait detection is a biometric technique that identifies individuals based on their walking patterns, which is useful for security, health monitoring, and forensic applications. This project focuses on developing a Python-based Gait Detection system using video analysis and deployed via a Streamlit web application. The system captures video input, extracts gait features such as stride length, joint movements, and walking rhythm, and applies machine learning or deep learning models to identify or classify individuals. Implemented using Python libraries like OpenCV, NumPy, Pandas, and TensorFlow/Keras, the system provides an interactive interface for real-time gait analysis, feature visualization, and identity recognition. The project enables automated, accurate, and non-invasive identification using gait biometrics.
Existing System
Traditional identification systems rely on fingerprint, face recognition, or manual observation for identification and monitoring. These systems can be intrusive, limited by occlusion or lighting conditions, and may not work in cases where facial or fingerprint data is unavailable. Existing gait recognition systems often depend on expensive hardware, controlled environments, or complex sensor setups, making them difficult to deploy in real-world scenarios. Additionally, many conventional approaches lack user-friendly software interfaces for real-time analysis or remote monitoring.

Proposed System
The proposed system implements a Python-based framework for gait detection using video streams and a Streamlit interface. Input videos are preprocessed to extract frames, normalize scale, and detect human silhouettes. Feature extraction techniques, such as pose estimation, joint tracking, and gait cycle analysis, are applied to capture unique walking patterns. Machine learning classifiers or deep learning models, including CNNs or LSTM networks, are trained to recognize individuals based on these gait features. The Streamlit application provides an interactive platform for uploading videos, processing gait analysis, and visualizing results with identification outputs and confidence scores. Python libraries such as OpenCV for video and image processing, TensorFlow/Keras for model training, NumPy and Pandas for data handling, and Streamlit for deployment are utilized. By combining gait feature extraction, machine learning classification, and an interactive web interface, the system offers an accurate, scalable, and non-intrusive solution for identity recognition and security monitoring.

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