Real-Time
Ambush Detection System
The Real-Time Ambush Detection system is designed to enhance security and prevent ambush attacks by analyzing real-time audio data from microphones. Using advanced signal processing and deep neural networks (DNN), the system detects human activities such as footsteps, whispering, and other subtle sounds, providing situational awareness and proactive threat detection.
How it works
The system captures and processes real-time audio data using advanced signal processing to enhance quality and extract features. Deep neural networks then analyze and classify sounds, detecting human presence and generating real-time alerts.
Data Acquisition
The system captures audio data from microphones, monitoring the environment for threats.
Preprocessing
Audio is preprocessed to enhance quality and reduce noise for better analysis.
Feature Extraction
Signal processing extracts audio features from data, focusing on characteristics of human activity.
Detection
DNN models analyze features to identify sounds that signify human presence, like footsteps or whispers.
Classification
The system classifies sounds into categories like speech and noise, enhancing audio understanding.
Decision Making and Reporting
The system analyzes classified sounds to assess threats, generate real-time alerts, and enhance AI accuracy.
Detection
DNN models analyze features to identify sounds that signify human presence, like footsteps or whispers.
Classification
The system classifies sounds into categories like speech and noise, enhancing audio understanding.
Decision Making and Reporting
The system analyzes classified sounds to assess threats, generate real-time alerts, and enhance AI accuracy.
Key features
Audio Detection: Identifies subtle human sounds.
Accurate detection under various conditions.
Real-Time Processing: Provides immediate alerts and analysis.
Compatible with various security systems.
Technical Skills
- Advanced Signal Processing
- Deep Neural Networks (DNN)
- Computer Vision and Image Processing
- Edge Devices
- Frameworks: TensorFlow, PyTorch
- Embedded Systems
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