Object Estimation Method for Edge Devices Inspired by Multimodal Information Processing in the Brain

To realize real-time mobile augmented reality applications, various objects in the real world need to be instantly identified, located, and represented as a digital twin through sensor devices and edge IoT systems. However, it is challenging to make a fast and accurate decision on what the object is from real-time noisy streaming information. Multimodal decision making has been expected to mitigate such incomplete information and improve the accuracy of simplified recognition algorithms tuned for edge devices. In this paper, we propose an object estimation method inspired from the multimodal information processing mechanism of the brain, which makes decisions based on multiple types of uncertain observed information. Through computer simulations, we show that our proposed method identifies an object accurately and quickly from uncertain observed information.