Cascaded Regional People Counting Approach Based on Two-Dimensional Spatial Attribute Features Using MIMO Radar

Abstract

A cascaded regional people counting approach based on two-dimensional (2-D) spatial attribute features using multi-input multi-output (MIMO) radar is presented in this letter. The main task is to detect whether there is a target, and to classify the number of people in a closed metal ramp. The difficulty to accurately detect the number of people lies in the presence of strong static clutter and the strong multipath effects in the environment. In this letter, range-angle spectrum is first computed and exploited to extract the 2-D spatial attribute features, namely the effective peaks of range-angle spectrum, the spacing features and the energy block features. These features reflect the relative position relationships between the people and the environment as well as the physical structure of a certain environment. Then, a maximum likelihood classifier and a random forest classifier are cascaded and applied to detect the target presence and classify the number of people. The experiment results show that the average classification accuracy of 0 person, 1 person, and multiple persons is above 92.55%, which outperforms the existing people counting approaches.