Smart Threat Detection & Alert System
Proactive AI Surveillance for Safer Public Spaces
Created on 30th December 2025
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Smart Threat Detection & Alert System
Proactive AI Surveillance for Safer Public Spaces
The problem Smart Threat Detection & Alert System solves
The Problem It Solves
Modern public spaces rely heavily on traditional CCTV surveillance systems that require continuous human monitoring. This approach is not only inefficient but also prone to human fatigue, delayed reactions, and missed threats.
- Security teams are often overwhelmed by:
- Hundreds of camera feeds to monitor simultaneously
- Delayed identification of unattended or suspicious objects
- Lack of intelligent prioritization of potential threats
- Reactive responses instead of proactive prevention
As a result, critical security incidents are often detected too late, reducing the effectiveness of existing safety infrastructure.
How This Project Helps
- The Smart Threat Detection & Alert System transforms passive surveillance into an intelligent, proactive safety solution.
What people can use it for:
- Monitoring airports, railway stations, campuses, malls, and public events
- Automatically detecting unattended or suspicious objects
- Receiving real-time alerts instead of manually watching camera feeds
- Improving response time during potential security threats
How it makes tasks easier & safer:
- Reduces human workload by automating surveillance
- Minimizes human error and fatigue
- Detects threats early, enabling faster intervention
- Filters false alarms using contextual analysis
- Operates in a privacy-first, non-intrusive manner (no facial recognition)
Key Impact
- By combining AI, computer vision, and real-time alerts, this system:
- Enhances public safety
- Improves situational awareness
- Enables faster, smarter decision-making
- Makes existing security infrastructure more effective
The result is a safer, smarter, and more reliable threat detection system suitable for real-world deployment.
Challenges we ran into
Challenges I Ran Into
Building an AI-powered, full-stack threat detection system within a limited hackathon timeframe presented several real-world engineering challenges. The project required seamless coordination between AI models, backend services, and frontend interfaces, all while ensuring real-time performance, reliability, and ethical constraints.
- Integrating AI Detection with Real-Time Backend Systems
Challenge:
The AI model was capable of detecting unattended or suspicious objects, but integrating its output with the backend for real-time analysis and alert generation was initially unreliable. Detection data sometimes arrived late, in inconsistent formats, or caused processing delays—making real-time alerting difficult.
How It Was Overcome:
Detection outputs were standardized into well-defined API response structures
Clear data contracts were established between the AI module and backend
Continuous log monitoring was used to trace data flow end-to-end
This ensured reliable, low-latency communication between detection and analysis layers, enabling accurate real-time alerts.
2.Frontend–Backend Communication & API Stability
Challenge:
The frontend dashboard occasionally failed to fetch data or trigger alerts correctly due to issues such as:
CORS misconfigurations
Incorrect API endpoints
Payload mismatches between frontend requests and backend expectations
These issues caused broken UI states and delayed responses.
How It Was Overcome:
Real-time debugging and network inspection were used to trace failed requests
API configurations were centralized to avoid inconsistencies
Backend validation and error handling were improved
This resulted in a stable and responsive frontend experience.
- Managing Multiple Services Under Hackathon Pressure
Challenge:
Running and coordinating multiple services simultaneously—AI model, backend server, and frontend application—under tight deadlines was challenging. Context switching between tools and terminals slowed down debugging and testing.
How KIRO IDE Helped:
Provided a single unified workspace for all components
Allowed running multiple services side-by-side using integrated terminals
Made it easy to monitor logs, restart services, and debug issues instantly
KIRO IDE significantly reduced setup overhead and saved valuable hackathon time.
- Debugging Configuration & Environment Issues
Challenge:
Environment variables, database connections, and runtime configurations caused unexpected failures during development, especially when switching between local and test environments.
How KIRO IDE Helped:
Centralized project structure made configuration issues easier to spot
Real-time logs exposed misconfigured variables and connection errors
Faster debugging cycles helped resolve issues without disrupting progress
This ensured smoother development despite time constraints.
- Reducing False Positives in Threat Detection
Challenge:
Initial AI detection results triggered alerts for non-threatening scenarios, leading to false positives that could reduce system trust in real-world deployment.
How It Was Overcome:
Introduced object persistence duration checks
Applied context-based rules instead of raw detection outputs
Tuned thresholds through rapid testing and iteration
KIRO IDE’s live reload and fast debugging enabled quick experimentation and fine-tuning during the hackathon.
Overall Impact of Using KIRO IDE
KIRO IDE played a critical role throughout the project by:
Accelerating development cycles
Reducing debugging and integration time
Enabling efficient multi-service management
Allowing the team to focus on problem-solving rather than tooling
KIRO IDE acted as a silent teammate, empowering rapid innovation, smooth integration, and efficient execution under hackathon pressure.
Tracks Applied (2)
Best Innovation
AWS
AWS