| I&T Solution |
Bus Stop Monitoring System
(REF: S-2057) |
| Trial Project |
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| Solution Feature |
- System achieves non-intrusive deployment through compact, modular camera units compatible with existing bus stop infrastructure at target Tung Chung East location. Adaptive imaging technology compensates for variable camera angles, lighting conditions, and environmental factors. Flexible power (AC/solar) and connectivity options ensure seamless integration without service disruption or structural modifications.
- Deploy synchronized multi-camera system with overlapping coverage to capture entire bus stop and queue areas. Utilize real-time cloud processing for occupancy detection, license plate and route recognition, bus arrival and departure logging with confidence scores, passenger queue count, and automated report generation with visual data overlays.
- Integrated cloud-based platform provides real-time dashboard displaying live occupancy levels, queue status, vehicle identification, peak period analytics, and historical trend analysis. Enables data export in standard formats and API for third-party platform integration and further analysis. Operators optimize service frequency and routes through data-driven congestion insights.
- System implements privacy-by-design with automated facial anonymization and optical character recognition masking, encrypted data transmission, and secure storage with role-based access controls. Ensures full compliance with local data protection regulations while maintaining occupancy mapping, queue analytics, and vehicle identification functionality throughout operation.
- Deep learning models achieve ≥90% accuracy across all key analytics: person detection and counting, bus identification, queue formation analysis. Models demonstrate robust performance across diverse environmental conditions and lighting variations. System includes continuous monitoring ensuring sustained accuracy and reliability throughout operational lifecycle.
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| Trial Application and Expected Outcome |
- Conduct a detailed site survey to assess structural integrity, power supply, network readiness, lighting conditions, and optimal mounting positions. Modular camera units with versatile mounting options. Evaluate connectivity solutions, implement adaptive image enhancement algorithms, and execute a pilot installation to validate feasibility and optimize deployment protocols.
- Deploy calibrated multi-camera system with overlapping coverage and synchronized capture via hardware triggers. Transmit video streams to cloud-based infrastructure utilizing edge computing for preliminary frame processing and noise reduction. AI algorithms analyze video feeds to count passengers, monitor queue formation and movement, identify arriving/departing buses.
- Design RESTful API specifications aligned with existing operational platform standards. Build a cloud-based dashboard displaying real-time occupancy metrics, queue analytics, vehicle identification, and historical trend data. Implement data export functionality supporting multiple formats (CSV, JSON) and third-party API connections for integration with external analytics tools.
- Apply real-time anonymization algorithms during edge and cloud video processing, enforce end-to-end encryption for all data flows, implement strict role-based access controls with continuous activity monitoring and audit trails, and establish comprehensive governance policies to ensure ongoing regulatory compliance and strict data minimization principles throughout the system lifecycle.
- Train advanced neural networks on annotated diverse video data using transfer learning targeting ≥90% accuracy. Implement active learning for model refinement. Conduct regular sample checks comparing live outputs against manually verified data to detect accuracy degradation. Maintain model versioning tracking improvements. Validate across scenarios before and during operation.
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| Additional Solution Information |
Build4Smart - Bus Stop Monitoring System.pdf
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| Info on I&T Solution Provider |
| Solution Provider | : | Build4Smart Limited | | Address | : | Unit 405, Nan Fung Commercial Centre, 19 Lam Lok Street, Kowloon Bay, Kowloon | | Contact Person | : | Olivier Yuk-ting Kwok |
| Position | : | Chief Executive Officer | | Tel | : | +85290414522 | | Email | : |
olivier@build4smart.com | | Webpage | : | www.build4smart.com |
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