A novel temporal-spatial object classification neural network model is proposed to improve the classification capability of tracked objects. It takes queued points of tracked objects using multiple frames as input, utilizes spatial and temporal information from these points for sampling and grouping as well as extracts hierarchical temporal-spatial features for target classification. Experimental results on a proprietary 4D Imaging Radar dataset and open-source 2D RadarScenes dataset demonstrate that the proposed tracker-cued radar point-cloud target classification method allows the model to learn meaningful appearance and motion features from sparse radar points data, and achieves accurate classification output as compared to a baseline method, while being efficient to run on edge hardware with limited resources.

This paper focuses on the challenge of estimating the 2D instantaneous ego -motion of vehicles equipped with an automotive radar. To further improve our previous study based on the weighted least squares (wLSQ) method and purpose-designed neural networks (NNs), this work proposes a new network architecture that supports local and global feature extraction as well as point-wise dynamic feature channel mixing. Compared with our previous work, the proposed method provides better estimation accuracy, lighter network size, and faster runtime performance.

A neural network (NN) based multi-frame classification approach is proposed to solve the problem of classification of tracked objects. Initially, a baseline tracker is implemented that uses the classification output of an object detection network for classification. Afterwards, two approaches for multi-frame classification are applied to perform classification of tracked objects. The first approach aggregates points from multiple frames and applies a single frame NN for classification, whereas the second approach uses bidirectional long short term memory (BiLSTM) layers to process points from multiple frames. Extensive experiments on the opensource 2D RadarScenes dataset showed a consistent increase in track performance when using either of the two techniques for multi-frame classification.