Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 50 | Crowdsurf: Realtime Crowd-Monitoring for indoor spaces |
Ananya Krishnan John Abraham Tanvika Boyineni |
Aniket Chatterjee | design_document1.pdf final_paper1.pdf photo1.jpeg photo2.PNG presentation1.pdf proposal1.pdf video |
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| Team Members: Tanvika Boyineni (tanvika3) Ananya Krishnan (ananya10) John Abraham (jabra6) Problem: Indoor public spaces (libraries, study lounges, gyms, student centers) often become congested, but students and facility staff lack real time, localized information about crowd density and traffic flow. Existing approaches either rely on cameras, raising privacy concerns, require manual observation, or provide only building level estimates that are not actionable for choosing a specific room/entrance. Solution: This project proposes a privacy preserving, real time crowd monitoring system that estimates occupancy and directional flow using distributed, non-imaging sensor nodes with local processing. Each node is deployed at an entrance or transition point and performs local detection and direction inference. Processed data is transmitted wirelessly to a central gateway, which aggregates occupancy estimates, logs data, and presents live metrics through a user facing dashboard. The system emphasizes robustness to sensor noise and communication loss, and ease of deployment. Solution Components: 1. Sensing Subsystem (Doorway Detection and Direction) -Non-imaging sensors per entrance mounted with spatial separation. -Direction inference using ordered sensor trigger -Calibration procedures for mounting height, angle, and baseline noise conditions. 2. Embedded Processing Subsystem -Microcontroller-based state machine for event detection, debouncing, and occupancy updates. -Filtering and gating logic to handle common edge cases such as pausing in doorways, close following individuals, and short reversals. -Node health monitoring, including sensor timeouts and heartbeat status. 3. Wireless Communication Subsystem -Packet structure includes timestamp, IN/OUT counts, current occupancy estimate, and node status. -Features such as retransmission, periodic heartbeats, and graceful degradation during packet loss. 4. Gateway and Data Logging Subsystem -Gateway device (like Raspberry Pi) receives telemetry from sensor nodes. -Maintains the system wide occupancy per entrance or room. -Logs data to persistent storage (CSV) and manages node reconnection. 5. Dashboard and User Interface Subsystem -Live dashboard displaying current occupancy, directional flow rate (people per minute), and recent trends. -Visual indicators for “crowded” vs. “not crowded” states based on configurable thresholds. 6. Hardware and PCB Subsystem (Sensor Node) -Custom PCB using a modular, low risk design approach -Mechanical enclosure and mounting plan to ensure consistent and repeatable sensor placement. Criterion for Success: The project will be considered successful if the system can accurately demonstrate real time directional counting and occupancy estimation at one to two doorways using non imaging sensors. The system must correctly track entries and exits and maintain a live occupancy estimate that updates within one second of a doorway event. A functional dashboard should display current occupancy, flow rate, and node status in real time, while the gateway continuously logs data for at least one hour without interruption. Additionally, a custom designed PCB must be fabricated and used for at least one sensor node in the final demonstration. The system must remain stable and operational during temporary wireless packet loss events, demonstrating graceful degradation without crashes and automatic recovery once communication resumes. Node health and connectivity status should be clearly visible through the user interface to allow for basic monitoring and debugging. If time permits, additional success criteria include scaling the system to three or four sensor nodes covering multiple entrances or zones, improving robustness in challenging edge cases such as tailgating or closely spaced groups, and evaluating accuracy as a function of traffic rate. Further extensions may include implementing battery-powered sensor nodes with basic power optimization strategies or adding simple short term congestion prediction based on recent occupancy trends. |
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