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People presence detection (visual wake word)

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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
Grayscale 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: 96×96

Memory footprint:
214 KB Flash for weights
37 KB RAM for activations

Accuracy: 87% against Coco subset dataset

Performance on STM32H747* @ 400 MHz
Inference time: 41 ms
Frame rate: 23.3 fps

Model: MobileNet v2 0.35 quantized

Input size: 128x128x3

Memory footprint:
403 KB Flash for weights
197 KB RAM for activations

Accuracy: 92% against Coco subset dataset

Performance on STM32H747* @ 400 MHz
Inference time: 145 ms
Frame rate: 6.8 fps

* As measured with STM32CubeAI 7.1.0 in FP-AI-VISION1 3.1.0