Comparison of Single Frame Classification with Micro-Doppler Classification of VRUs for Traffic Radar

Protecting Vulnerable Road Users (VRUs) is a critical concern, particularly in the context of autonomous driving at intersections, where the environment becomes complex and challenging. Various considerations, including accuracy, reliability, and system processing time, are crucial for VRUs classification models. This paper aims to assess the feasibility of state-of-the-art models in real-world scenarios, comparing two VRU classification methods using a stationary radar: a single-frame approach and a multi-frame micro-doppler method. The single-frame method employs a Convolutional Neural Network (CNN) to a cropped Range-Doppler map (RD map) around the target position, while the micro-doppler classification considers both a CNN network and a CNN-LSTM network and uses the micro-doppler spectrogram as an input. Focused on pedestrians, vehicles, and cyclists. The results, validated through a 5-fold cross-validation and F1 score, highlight the superiority of the single-frame classification in real-world scenarios, offering instantaneous and effective multi-class multi-object object classification.