- The use of micro-processor is tremendously increasing in the era of smart and connected world.
- With the increase in amount and velocity of data, it is required to process the data in a real time manner (or near to real time) to generate the actionable events from the data using the low-cost microprocessor.
- Use case focused on cutting edge areas i.e. Internet of Things, Image processing, and Data Analytics etc.
- Ability to deal with both structured (i.e. motion detection true/false) and unstructured (i.e. image) data.
- It can also be applicable to scenario where limited internet connectivity is available (by implementing the local broker based MQTT implementation).
Hardware, Tools and Technology Used
- Raspberry Pi-3
- Raspberry Pi camera module
Technology (library) used:
- Node.js- A Lightweight scripting language with small memory footprint
- Opencv*- Computer Vision library
- Node opencv module** to utilize the power of image processing on micro-controller
- Node rasipcam^ module to enable the functionality of Raspberry Pi camera module
*Installation steps are available at http://www.pyimagesearch.com/2015/02/23/install-opencv-and-python-on-your-raspberry-pi-2-and-b/
**Node opencv module is available at https://www.npmjs.com/package/opencv
^Node raspicam module is available at https://www.npmjs.com/package/raspicam
- In the current scenario, Node.js code captures the image periodically and count the head using image processing technique (limitation).
- Notifying the camera module to capture the image if motion is detected by PIR Motion sensor (in-progress).
- Detecting the common faces in the two corresponding images and ignore them for head count.
- Developing HVAC application (ideal scenario of shopping mall and auditorium- How to manage the HVAC with dynamic visitors/participants) by re-using the architecture (sounds interesting ???).