The Challenge
Traditional sentry guns face several limitations:
- Complex manual setup requirements
- Lack of autonomous target identification
- High costs limiting accessibility
- Inflexible targeting parameters
- Limited educational application potential
Sentry Nerf Gun
Developing a computer vision system for autonomous target acquisition and tracking, creating an intelligent platform that can identify and engage targets in real-time.
Detection Accuracy
95%
Target detection in optimal conditions
Response Time
200ms
Average time from detection to aiming
Shooting Accuracy
85%
Hit rate on moving targets at medium range
The Solution
Development of a computer vision-based system that:
- Autonomously identifies human targets
- Tracks movement in real-time
- Uses affordable, accessible hardware
- Operates effectively in variable environments
- Provides a platform for educational exploration
Computer Vision Detection System
The physical platform features a sophisticated mechanical system designed for precision and reliability:
- Dual-axis servo-controlled gimbal for precise aiming
- Custom-designed mounting brackets for stability
- Motorized trigger mechanism for remote activation
- Integrated camera mount aligned with the barrel
- All components designed to minimize mechanical play that could affect accuracy
Technical Implementation
The system utilizes a Raspberry Pi as the central processing unit, running Python-based computer vision algorithms through OpenCV. The camera feed is processed in real-time using a sophisticated detection pipeline.
The detection process involves background subtraction to isolate moving objects, contour detection to identify human-shaped silhouettes, Kalman filtering for smooth target tracking, and decision algorithms to determine targeting priority.
Servo-Controlled Aiming Mechanism
Applications
Educational Applications
- Computer vision fundamentals
- Real-time control systems
- Embedded programming techniques
- Mechanical systems integration
Future Development
- Enhanced target classification using machine learning
- Improved tracking algorithms for faster response
- More sophisticated decision-making for multi-target scenarios
- Energy optimization for extended operation