Swift-Spatio Flow: A Human Action Recognition Model Using 3D CNN-LSTM

📌 Project Overview

Swift-Spatio Flow is an advanced Human Action Recognition (HAR) model that combines 3D Convolutional Neural Networks (3D CNN) with Long Short-Term Memory (LSTM) networks. This project aims to improve action recognition in videos by efficiently extracting spatial and temporal features while reducing computational cost.

🚀 Key Applications

• CCTV surveillance enhancement
• Assisting the visually impaired
• Self-driving cars
• Sports analytics

🎯 Problem Statement

Existing HAR models suffer from:

• Complexity: High computational cost
• Accuracy: Difficulty in handling low-quality videos
• Scalability: Struggle with large datasets

Swift-Spatio Flow addresses these challenges by integrating a 3D CNN and LSTM to extract spatial and temporal features efficiently.

📊 Methodology

1. Preprocessing:

   • Extract frames from videos
   • Resize and normalize images
   • Convert frames into sequences

2. Model Architecture:

   • 3D CNN for feature extraction
   • LSTM for sequence modeling
   • Softmax activation for classification

3. Training & Evaluation:

   • Dataset: UCF-50
   • Metrics: Accuracy, Precision, Recall, F1-score
   • Comparison with existing models

🏆 Results

Model	Accuracy (%)	Precision (%)	Recall (%)	F1 Score (%)
CNN + LSTM	76.12	75.94	74.17	75.86
ConvLSTM2D	78.95	78.74	76.14	78.68
Time Distributed CNN	88.50	88.00	87.52	87.71
3D CNN (UCF-101)	91.65	89.96	90.82	91.10
Swift-Spatio Flow	94.89	94.37	93.45	93.56

🔮 Future Enhancements

Train on larger datasets like Kinetics for better generalization Optimize computational cost for real-time performance Deploy the model as a web application

🤝 Contributors

• Ian Joseph K
• Aryan Patil (https://github.com/aryanator)
• Abhishek Raje
• Ramani Harsh Anilkumar