Suspicious Human Activity Detection

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Suspicious Human Activity Detection

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₹4,999.00

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Overview
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Detail Description

1. Abstract

The Suspicious Human Activity Detection System is an AI-based application designed to identify potentially unusual activities in video surveillance footage using computer vision and deep learning techniques. The system analyzes human movements and activity patterns to flag events that may require further review, such as unauthorized entry, prolonged restricted-area presence, or unusual movement patterns. When a predefined event is detected, the system can generate an alert for security personnel. This project aims to support security monitoring and improve the efficiency of video surveillance systems.

2. Objectives

To detect people and track their movements in video streams.
To identify predefined unusual activities using AI techniques.
To monitor activity in designated areas.
To generate alerts when configured activity rules are triggered.
To reduce the workload involved in manual video monitoring.
To support security personnel with automated event analysis.
To maintain event logs for further review.

3. Existing System

Traditional surveillance systems rely mainly on security personnel who continuously observe live camera feeds or review recorded footage. This approach can make it difficult to identify important events across multiple cameras and long recording periods.

Disadvantages of the Existing System:

Requires continuous human monitoring.
Time-consuming review of recorded video.
Important events may be missed during prolonged monitoring.
Difficult to monitor multiple cameras simultaneously.
Limited automated activity analysis.
Delayed identification of predefined security events.

4. Proposed System

The proposed system uses AI, computer vision, and human activity recognition to detect people, track movements, and identify predefined events in video footage. It analyzes activity patterns and compares them with configured rules or trained model predictions. When a potential event is identified, the system generates an alert and stores relevant information for authorized review.

Key Features:

Real-time human detection and tracking.
Video-based activity recognition.
Restricted-area entry detection.
Detection of unusual movement patterns.
Configurable event rules and alert thresholds.
Alerts for security personnel.
Event history with timestamps.
Dashboard for viewing flagged events and reviewing video clips.

5. Implementation Procedure

Requirement Analysis: Define the target environment and the specific activities that need to be detected.
Dataset Collection: Collect suitable surveillance videos or public human activity datasets.
Video Input Setup: Connect a camera or upload recorded video footage.
Frame Processing: Use OpenCV to extract and process video frames.
Human Detection: Apply a pretrained model, such as YOLO, to identify people in each frame.
Object Tracking: Use an appropriate tracking algorithm to follow detected people across consecutive frames.
Activity Analysis: Analyze movement patterns, human poses, or predefined rules to identify configured events.
Event Classification: Use a trained activity recognition model or rule-based logic to flag potential events.
Alert Generation: Display notifications when configured conditions are met.
Dashboard Development: Create an interface to display live footage, event logs, and flagged clips.
Testing and Deployment: Evaluate detection accuracy, false alarms, lighting variations, and crowded environments.

6. Software Requirements

Operating System: Windows 10/11 or Linux
Programming Language: Python
Computer Vision Library: OpenCV
Object Detection: YOLO
Object Tracking: Deep SORT or ByteTrack
Deep Learning Framework: PyTorch or TensorFlow
Pose Estimation: MediaPipe (optional)
Data Processing: NumPy and Pandas
Frontend: Streamlit, Flask, or React.js
Database: SQLite or MySQL
Development Environment: VS Code or PyCharm

7. Hardware Requirements

Processor: Intel Core i5 or equivalent recommended
RAM: Minimum 8 GB recommended
Storage: At least 10 GB of available space
Camera: Webcam or surveillance camera
Display: Monitor or laptop display
Input Devices: Keyboard and mouse
Network Connection: Required for network cameras or remote alerts
GPU: Optional for lightweight prototypes; a compatible GPU is recommended for demanding real-time video analysis.

8. Advantages of the Project

Supports automated surveillance monitoring.
Helps identify predefined security events.
Reduces the workload of manually reviewing footage.
Enables faster notification of flagged events.
Maintains event logs for later investigation.
Supports monitoring across different environments.
Can be integrated with existing video surveillance systems.
Demonstrates practical applications of AI and computer vision.

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