Platform development
Develop an AI-enabled safety and security platform
CASE STUDY | SAFETY CLARITY
Enhancing Safety and Security Through Intelligent Monitoring
Transforming safety and security systems monitoring across live rail operations with AI-enabled analytics
Faster incident response reduced from minutes to seconds
Automated vigilance across stations
Reduced manpower dependency for continuous surveillance operations
Decreased near-miss incidents and unauthorised access events
THE CHALLENGE
Critical incidents are often only seen after they begin
Hundreds of video feeds must be monitored simultaneously
Track intrusion and unsafe passenger behaviour require instant detection and intervention
Underutilisation of video data for proactive analysis and prevention

Engineering intelligence into existing surveillance systems
Develop an AI-enabled safety and security platform
Leverage deep learning models to detect anomalies via iSafe
Man-in-the-loop model ensures human operators maintain oversight
Single dashboard view across surveillance network and analytics
Digital twin visualisation for rapid situational awareness
KEY INSIGHT
Video data becomes actionable intelligence when AI is integrated into existing operational workflows. This enables safer operations without additional manpower and shows how meaningful transformation can be achieved without rebuilding infrastructure.

TARGETED INTERVENTIONS
Deploys computer vision and deep-learning models to detect track intrusions; unsafe loitering; and slip, trip and fall incidents
Integrates with Operations Control Centre workflows for alert generation within seconds
Ensures operators retain decision authority and escalation control
Provides a single operational view across CCTV networks via a centralised platform
Augments existing surveillance infrastructure without requiring large-scale replacement
REGULATORY PRIORITIES
01
Secure architectures aligned with cybersecurity and data protection standards
02
All incidents and system actions provide full traceability and auditability
03
Monitoring processes align strictly with established rail safety management systems
04
Detection models were validated under real operating conditions in phases to ensure reliability
AI & computer vision + Smart surveillance & video analytics + Digital command & control + Real-time incident detection + Brownfield system integration
OUTCOMES
Faster incident response reduced from minutes to seconds
Automated vigilance across stations
Reduced manpower dependency for continuous surveillance operations
Decreased near-miss incidents and unauthorised access events
Mr Goh Khim Wah
Chief Controller, Bukit Panjang LRT
Let's turn passive surveillance into real-time actionable intelligence.