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Smart Port Challenge
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Smart Port Challenge 2026
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Intelligent Port Perimeter Threat Detection

Intelligent Port Perimeter Threat Detection

Thematic Area: Next Generation Port

16) How might we design/engineer an intelligent security system that automatically correlates suspicious vehicular activity outside our perimeter (e.g., loitering, drop-offs) with potential human intrusion attempts at the fence line, enabling us to identify and neutralize coordinated threats before a breach occurs?

BACKGROUND

To move beyond simple intrusion detection to the pre-emption of coordinated threats.

The goal is to deploy a system that automatically identifies and links suspicious vehicular activity with potential human intruders, reducing threat identification time from minutes to seconds and increasing the probability of interdiction before a breach occurs.

SIGNIFICANCE OF PROBLEM

  • Filtering out the high volume of legitimate vehicle and human traffic near the port to minimize false positives.
  • Maintaining high performance across variable weather and lighting conditions (e.g., rain, night, shadows).
  • Ensuring real-time processing and low-latency alerts to enable rapid response.

POTENTIAL MARKET SIZE

The core addressable market for integrated, AI-driven predictive perimeter security dedicated specifically to marine ports and adjacent critical logistics infrastructure sits between USD 450 million and USD 750 million globally for 2026.

This specific niche is expected to grow at an aggressive Compound Annual Growth Rate (CAGR) exceeding 22% through 2030. Growth is primarily fuelled by tightening international security mandates (such as the ISPS Code), the rising costs of manual security details, and a profound shift toward automated systems capable of neutralizing coordinated breaches before the perimeter is physically compromised.

EXISTING EFFORTS

AI platforms can continuously analyse video and sensor data from the perimeter. When the system detects a suspicious vehicle (e.g., loitering) and a nearby human presence, it automatically fuses the events, flags it as a “Coordinated Threat,” and presents a single, actionable alert to the Security Control Centre (SCC) operator with tagged video evidence from both events.

To address this further, there must be seamless integration with Jurong Port’s existing video management systems and camera infrastructure. High-accuracy AI models for both nuanced vehicle behaviour analytics (circling, stopping in no-stop zones) and human detection/posture analysis (crouching, climbing). AI-driven Video Analytics Platforms with advanced behavioural analysis capabilities. Sensor Fusion Startups capable of combining video with other data streams (e.g., LiDAR, radar) for greater context, with successful detection rate of at least 90%.