schollz/howmanypeoplearearound — explained in plain English
Analysis updated 2026-06-22
Monitor foot traffic at a home or small venue using a Raspberry Pi running in the background
Check whether roommates or people are in a shared space without asking
Log crowd level data over time and visualize how foot traffic changes throughout the day
| schollz/howmanypeoplearearound | roboflow/rf-detr | yangchris11/samurai | |
|---|---|---|---|
| Stars | 7,061 | 7,063 | 7,065 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | moderate | hard |
| Complexity | 2/5 | 3/5 | 4/5 |
| Audience | developer | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires a WiFi adapter that supports monitor mode (most built-in laptop cards do not) and tshark installed separately.
This Python tool estimates how many people are physically nearby by counting smartphones detected over WiFi. It works by listening for WiFi probe requests, which are short signals that phones automatically broadcast when searching for known networks to connect to. By counting the distinct devices sending those signals, the tool produces a rough estimate of how many people with smartphones are in the vicinity. Since most people carry a smartphone, this acts as a practical proxy for crowd size. The README opens with a clear legal warning: intercepting WiFi signals from networks you do not own may be illegal depending on your country. In the United States it may violate federal wiretapping law, and the repository links to a discussion thread about the legal risks. This is a tool for monitoring your own space, not public networks. To run it you need a WiFi adapter that supports monitor mode, a special capability that most built-in laptop cards do not have but inexpensive USB adapters do. The README lists specific adapter models and chipsets that are known to work. You also need tshark installed, which is the command-line version of Wireshark, a standard network analysis tool. Installation is a single pip command. Once running, you type one command in a terminal, wait for the scan period to complete, and get a number printed back: something like "There are about 3 people around." Options let you set the scan duration, restrict the count to only nearby devices based on signal strength, output the raw data as a JSON file, or loop the scan indefinitely. If you collect data over time with the loop option, the tool can render a simple browser-based chart showing how crowd levels changed throughout the day. Practical use cases mentioned in the README include tracking foot traffic at home with a Raspberry Pi or checking whether roommates are in. The tool is licensed under MIT.
A Python command-line tool that estimates how many people are physically nearby by counting smartphones detected via WiFi probe requests, the signals phones broadcast when searching for known networks.
Mainly Python. The stack also includes Python, tshark, Wireshark.
MIT License, use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.