Social distance estimation system using low-resolution IR images with Boosting-CNN
Kiichi Ogawa, Naoyuki Morimoto · 2023
Low-resolution infrared (IR) images potentially provide information for estimating social distance between people without capturing personally identifiable information. In this study, we have implemented a social distance estimation system using Boosting-CNN (AdaBoost-based convolutional neural network). Furthermore, the system is embedded on a low-power microcontroller. We have compared the inference time between Boosting-CNNs and single CNNs processed on the microcontroller, and have found that specific Boosting-CNNs had similar performance and relatively shorter inference time than single CNNs. We have also constructed a Boosting-CNN using CNN models with different parameters, and it has been observed that the accuracy is improved by the order of training.