On the Use of Machine Learning and Deep Learning for Radar- Based Passenger Monitoring

Abstract

In the past decade, there has been an increasing demand for in-vehicle safety sensors. In this paper, we use a multi-input multi-output (MIMO) frequency modulated continuous wave (FMCW) radar for in-vehicle passenger detection and occupant type classification. We propose a Convolutional Long Short-Term Memory (ConvLSTM) that requires no handcrafted rules and accurately detects passengers and classifies occupant type (empty/adults/children). Our model shows high precision (0.90) and recall (0.95) when used to detect unattended children in the vehicles.

Jennifer Yu

MSc. Student

My research interests include distributed robotics, mobile computing and programmable matter.