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Case study
VastNext AI
The Challenge
Ferry operations needed a reliable way to track passenger flow at the counter and ensure safe capacity limits. Manual counting was error-prone and made it difficult to maintain accurate, auditable records.
- Accurate counting of people entering and exiting the counter area
- Real-time visibility into current onboard/queue counts
- Automatic enforcement of capacity limits to prevent over-boarding
- Persisting counts to a database for reporting and auditing
Our Solution
We built VastNext AI, an automation system that uses computer vision to count passengers in real time and synchronizes the results with a database-backed operational dashboard.
- Real-time people counting for both entry and exit directions
- Database persistence for historical counts, reporting, and auditing
- Capacity threshold logic with alerts/lockout signals when limits are reached
- Operator-friendly dashboard for live status and history
Challenges We Overcame
- Counting accuracy: Handling occlusions, lighting changes, and varied passenger flow patterns
- Real-time performance: Keeping latency low enough for operational decision-making
- State consistency: Ensuring reliable database updates and recovery from intermittent connectivity
- Operations UX: Presenting clear, actionable status and alerts for staff
Technology Stack
Python
TensorFlow
React
PostgreSQL
AWS
D3.js
Results & Impact
- Improved accuracy and consistency versus manual counting
- Real-time capacity visibility to prevent over-boarding
- Database-backed reporting for operations and compliance
- Reduced workload for counter staff during peak periods
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