Making homes and buildings smarter means offering an occupant-centric management of the environment we live and work in. This paradigm shift requires enhanced sensing intelligence in the surrounding electronic components. In this context, people presence detection opens up new possibilities to make lighting, heating, air conditioning applications smarter and more efficient.
Approach
– We used of a camera module (B-CAMS-OMV) to capture the scene and scaled down to 96×96 pixels
– We selected a pre-trained NN model from Google visual wake word to manage presence detection
– The model is already integrated in the function pack FP-AI-VISION1 (made for STM32H747 discovery kit)
– The model is optimized using STM32Cube.AI
Sensor
Vision: camera module bundle (reference: B-CAMS-OMV)
Data
Data format:
2 classes: people / no-people
Color image 96×96 image for MobileNet v1 0.25
Color image 128×128 for MobileNet v2 0.35
Results
Model: MobileNet v1 0.25 quantized
Input size: 96x96x3
Memory footprint:
214 KB Flash for weights
40 KB RAM for activations
Accuracy: 85% against Coco subset dataset
Performance on STM32H747* @ 400 MHz
Inference time: 36 ms
Frame rate: 28 fps
Model: MobileNet v2 0.35 quantized
Input size: 128x128x3
Memory footprint:
402 KB Flash for weights
224 KB RAM for activations
Accuracy: 91% against Coco subset dataset
Performance on STM32H747* @ 400 MHz
Inference time: 110 ms
Frame rate: 9 fps
* As measured with STM32CubeAI 7.1.0 in FP-AI-VISION1 3.1.0
