Artificial Intelligence is quickly becoming an important ingredient for IoT projects’ success, a requirement for unlocking its full potential and providing a competitive edge to those that embrace it. AI naturally integrates into existing connected sensor/actuator networks and immediately adds measurable value. It was hard to imagine until recently, that AI will be so easily accessible, with just a few libraries installed we can take benefit of this amazing technology.
Let me illustrate with a simple example – enhancing ordinary IP security cameras with AI. The goal is that the cameras will recognize the objects they see and publish the recognized object’s data to an MQTT topic. I am interested in the detected object’s type, location within the captured frame and recognition confidence level. The applications I am considering are:
- Object following via PTZ (keeping the object of interest e.g. human in the middle of the frame)
- PTZ camera blind spot avoidance with opposing cameras providing each other that object of interest is beyond their current viewing angle
- Smart movement detection i.e. only trigger alarm event if certain object types are detected
- Monitor our presence at home vs detected object of risky type, i.e. I don’t want to see humans wandering the yard while we are at work, or during nights while the security system is armed
- Providing the object’s data over MQTT so other IoT nodes can make decisions/trigger actions
The project relies on image classification with deep convolutional neural networks using the darkflow library. Setting up darkflow is pretty trivial, simply follow the instructions in the repository. I used Python, basically, it starts an FFmpeg background process that captures a single frame from the RTSP stream every 5 seconds and passes it to darkflow for analysis. The analysis results are then published to an MQTT topic, and as an option, a screenshot of the detected objects is taken.
The reason why I used FFmpeg rather than OpenCV to capture the RTSP frames is that OpenCV implements RTSP over TCP by default. My specific camera model only provides RTSP over UDP. I tried to recompile with UDP support, but could not get it to work reliably. I use a RAM drive to avoid excessive writes on my SSD. Ideally, this setup will run on a Raspberry Pi 3 B+, I have one in order.
My code is available here for you to try out.
Couple screenshots, note how the detected objects are outlined, labeled and confidence level visualised:
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