Conventional radar-based people counting systems are designed mainly for dense spatial
distributions in a small region of interest (ROI). Therefore, a system with only conventional …

In this paper, we propose a novel people counting algorithm exploiting convolutional neural
network (CNN) using a low radiation impulse radio ultra-wide bandwidth (IR-UWB) radar …

People counting is one of the hottest issues in sensing applications. Impulse radio
ultrawideband radar has been extensively adopted to count people because it provides a …

In this paper, we propose system based on impulse radio ultra-wideband (IR-UWB) radar
sensors for counting multiple people passing through a passage or a wide door. The …

In this paper, we propose a people counting algorithm using an impulse radio ultra-
wideband radar sensor. The proposed algorithm is based on a strategy of understanding the …

People counting serves a vital role in sensing applications. Impulse radio ultra-wideband (IR-
UWB) radar, which has strong penetration and high-range resolution, has been extensively …

With the development of deep learning (DL) frameworks in the field of pattern recognition,
DL-based algorithms have outperformed handcrafted feature (HF)-based ones in various …

Counting people and things (targets) in a monitored area, also known as crowd-counting,
enables several applications in diverse scenarios, including smart building, intelligent …

Reliable people counting is crucial to many urban applications. However, most existing
people counting systems are sensor-based and can only work in some fixed gateways or …

People counting is key to a diverse set of sensing applications. In this paper, we design a
mote-scale event-driven solution that uses a low-power pulsed radar to estimate the number …