# schollz/howmanypeoplearearound

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7,074 stars · 378 forks · Python · MIT

## Links

- GitHub: https://github.com/schollz/howmanypeoplearearound
- awesome-repositories: https://awesome-repositories.com/repository/schollz-howmanypeoplearearound.md

## Topics

`location` `sensor` `tshark` `wifi`

## Description

This project is a crowd density estimator and WiFi probe request monitor that calculates approximate person counts by analyzing MAC addresses and signal strength from nearby devices. It functions as a packet capture tool that detects smartphone WiFi signals to estimate the number of people in a surrounding area.

The system includes a network presence visualizer that uses browser-based plotting to track device trajectories and occupancy trends over time. It also serves as a data capture utility that saves detected device manufacturer details and signal data into structured JSON files for further analysis.

The software covers crowd density estimation, IoT people counting, and wireless device tracking. It implements these through WiFi-based occupancy monitoring and the analysis of wireless probe requests.

## Tags

### Hardware & IoT

- [Wireless People Counters](https://awesome-repositories.com/f/hardware-iot/wireless-people-counters.md) — Calculates the total number of people in a specific area by monitoring wireless probe requests. ([source](https://github.com/schollz/howmanypeoplearearound#readme))
- [Crowd Occupancy Analyzers](https://awesome-repositories.com/f/hardware-iot/crowd-occupancy-analyzers.md) — Determines the approximate number of people in the surrounding area using signal density from smartphones. ([source](https://github.com/schollz/howmanypeoplearearound/search))
- [Non-Visual Presence Detectors](https://awesome-repositories.com/f/hardware-iot/non-visual-presence-detectors.md) — Provides a browser-based tool to track device trajectories and occupancy trends using non-optical signals.

### Artificial Intelligence & ML

- [Signal-Based Density Estimation](https://awesome-repositories.com/f/artificial-intelligence-ml/density-estimation/crowd/signal-based-density-estimation.md) — Tracks how many people are present in a specific location using wireless signal analysis and device counting.

### Data & Databases

- [MAC Address Tracking](https://awesome-repositories.com/f/data-databases/naming-conventions/device-identifier-mappings/mac-address-tracking.md) — Tracks unique hardware MAC addresses over time to ensure each physical device is counted as a single person.
- [Temporal Trend Visualizers](https://awesome-repositories.com/f/data-databases/data-visualization-charts/time-series-visualizers/temporal-trend-visualizers.md) — Generates browser-based plots from signal data to track person counts and device movement over time. ([source](https://github.com/schollz/howmanypeoplearearound/blob/master/README.md))

### Graphics & Multimedia

- [People Counters](https://awesome-repositories.com/f/graphics-multimedia/image-editing-processing/image-processing/people-counters.md) — Automates the process of counting humans in a space without cameras by using monitor-mode network adapters.
- [Occupancy Plotters](https://awesome-repositories.com/f/graphics-multimedia/occupancy-plotters.md) — Provides browser-based plots that render device trajectories and occupancy trends over time.

### Networking & Communication

- [Probe Request Analyzers](https://awesome-repositories.com/f/networking-communication/probe-request-flooding/probe-request-analyzers.md) — Isolates management frames sent by mobile devices to identify unique hardware addresses.
- [Wireless Packet Capturing](https://awesome-repositories.com/f/networking-communication/wireless-packet-capturing.md) — Intercepts raw 802.11 wireless frames from a network adapter in monitor mode to capture device signals.
- [Packet Capture Utilities](https://awesome-repositories.com/f/networking-communication/packet-capture-utilities.md) — Saves detected device manufacturer details and signal data into structured JSON files for analysis.

### Security & Cryptography

- [RSSI Proximity Estimators](https://awesome-repositories.com/f/security-cryptography/device-security-signals/proximity-signals/rssi-proximity-estimators.md) — Uses received signal strength indicators to approximate the distance and presence of devices.
- [WiFi Reconnaissance](https://awesome-repositories.com/f/security-cryptography/multi-vector-wifi-attack-suites/wifi-attack-toolkits/wifi-reconnaissance.md) — Passively scans and enumerates nearby smartphones via probe requests to estimate crowd size.
- [Occupancy Monitoring](https://awesome-repositories.com/f/security-cryptography/multi-vector-wifi-attack-suites/wifi-attack-toolkits/wifi-reconnaissance/occupancy-monitoring.md) — Estimates the number of people in a physical area by detecting WiFi probe requests.