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The 2019 miniproject involves taking readings from a sensor on an IoT device, processing the data onboard the IoT device and sending data to a cloud service for plotting/viewing. This miniproject uses a Raspberry Pi Zero W with a PiCamera module to count cars passing by on a street or highway. We show examples of uploading data to Azure IoT. Feel free to use another cloud service if you prefer.
This plot shows the motion signal from the PiCamera library. The motion data is a high SNR signal for stable camera platforms. Cars normally travel in lanes -- a parallel 1-D problem!
Many cites and localities are interested in improving public health by encouraging larger proportions of bicycles, scooters, walking and mass transportation vs. automobiles. According to WHO 2016, 92% of the world's population lives in areas with air pollution exceeding WHO limits. One way to understand sources/trends driving local air pollution is by monitoring vehicle traffic vs. time.
Having an inexpensive sensor that could be stuck almost anywhere is useful. It's also useful to have a low cost sensor. An IMX219 camera module is offered for about $8 on Alibaba, and the Raspberry Pi Zero W is widely available for $5. A small 1200 mW solar panel and 5000 mAh battery is available for about $10, so for about $25 in quantity, one might have a widely deployable traffic activity monitor based on the Raspberry Pi Zero W.
This project aims to count automobiles (trucks, cars) over time on city streets near the University.
This project assumes the student will know how to use essential terminal-based programs including ssh.
Due to the limited time for this project, we make simplifying assumptions:
- The system can work only during daylight hours or alternatively nighttime as the algorithms may differ from day to night
- System need only work in one location with a good street/highway view
- a good weather day (no rain, fog etc.)