Human Activity Recognition from CCTV Footage

 

Project Synopsis

Title: Human Activity Recognition from CCTV Footage

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1. Introduction

Human Activity Recognition (HAR) plays a crucial role in intelligent surveillance systems, smart cities, and public safety. With the rapid deployment of CCTV cameras in public and private spaces, there is a growing demand for automated systems that can monitor, analyze, and recognize human activities in real time. Such systems can be used for crime detection, anomaly detection, crowd monitoring, and workplace safety.

This project focuses on building a machine learning and deep learning-based HAR framework capable of recognizing different human activities (e.g., walking, running, sitting, fighting, loitering) from CCTV video footage.

 

2. Problem Statement

Traditional CCTV surveillance relies on human operators to monitor video streams, which is time-consuming, error-prone, and inefficient. Manual monitoring:

• Leads to missed incidents due to operator fatigue.
• Cannot provide real-time alerts.
• Struggles with large-scale camera networks.

Therefore, there is a need for an automated activity recognition system that can process CCTV footage and classify human activities accurately and in real time.

 

3. Objectives

• To preprocess CCTV video data and extract relevant features.
• To implement deep learning-based models (CNN, RNN, LSTM, 3D-CNN) for activity recognition.
• To classify activities such as walking, running, fighting, falling, or suspicious movements.
• To generate real-time alerts for abnormal or suspicious activities.
• To evaluate the system using accuracy, precision, recall, F1-score, and confusion matrix.

 

4. Proposed Approach

1. Data Collection & Preprocessing
• Use public HAR datasets (UCF101, Kinetics, HMDB51, custom CCTV dataset).
• Perform frame extraction, resizing, background subtraction, and normalization.
2. Feature Extraction
• Apply CNN-based spatial feature extraction.
• Use temporal modeling with RNN/LSTM or 3D-CNN for motion features.
3. Activity Recognition Model
• Train and test deep learning models for activity classification.
• Fine-tune models with transfer learning (e.g., ResNet, Inception, MobileNet).
4. Anomaly Detection
• Implement unsupervised models (e.g., Autoencoders, One-Class SVM) for suspicious activity recognition.
5. System Deployment
• Integrate the trained model with CCTV video streams.
• Real-time processing and alert generation for abnormal activities.

 

5. Expected Outcomes

• A working HAR system capable of classifying normal activities (walking, sitting, running) and detecting abnormal activities (fighting, falling, intrusion).
• Improved surveillance automation and reduced human workload.
• Enhanced public safety and security monitoring.
• Real-time activity recognition with high accuracy.

 

6. Tools & Technologies

• Programming Languages: Python
• Deep Learning Libraries: TensorFlow, Keras, PyTorch
• Computer Vision Tools: OpenCV, MediaPipe
• Datasets: UCF101, HMDB51, Kinetics dataset, custom CCTV dataset
• Deployment: Flask/Django (for web integration), GPU-enabled environment for training

 

7. Applications

• Smart city surveillance systems
• Crime prevention and anomaly detection
• Crowd monitoring in public places (railway stations, airports, malls)
• Elderly care and fall detection in healthcare
• Workplace safety monitoring (factories, construction sites)

 

8. Conclusion

This project aims to develop a real-time Human Activity Recognition system using CCTV footage and advanced deep learning models. The system enhances surveillance by automatically detecting and classifying human activities, thereby improving safety, security, and efficiency in monitoring environments.