Person Detection Using an Ultra Low-resolution Thermal Imager on a Low-cost MCU
This enables low-cost, embedded person detection systems for applications requiring cheap sensors and simple hardware, though it is incremental in adapting existing CNN methods to constrained environments.
The paper tackles person detection using an ultra-low-resolution thermal imager on low-cost microcontrollers, achieving up to 91.62% accuracy (F1-score) with a model under 10k parameters and runtimes as fast as 46ms.
Detecting persons in images or video with neural networks is a well-studied subject in literature. However, such works usually assume the availability of a camera of decent resolution and a high-performance processor or GPU to run the detection algorithm, which significantly increases the cost of a complete detection system. However, many applications require low-cost solutions, composed of cheap sensors and simple microcontrollers. In this paper, we demonstrate that even on such hardware we are not condemned to simple classic image processing techniques. We propose a novel ultra-lightweight CNN-based person detector that processes thermal video from a low-cost 32x24 pixel static imager. Trained and compressed on our own recorded dataset, our model achieves up to 91.62% accuracy (F1-score), has less than 10k parameters, and runs as fast as 87ms and 46ms on low-cost microcontrollers STM32F407 and STM32F746, respectively.