RPT²: Integrating Radar-Point Transformer and Tracker for Efficient Moving Object Recognition

Object detection from radar data is a crucial task for understanding dynamic environments, particularly in adverse weather conditions where other sensors may struggle. However, predicting bounding boxes (BBoxes) from radar point clouds poses significant challenges due to the sparsity and high noise levels inherent in radar data. In this paper, we introduce RPT<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, a novel radar BBox prediction framework that integrates a radar point transformer with a point-based tracker to effectively leverage spatial and temporal information. The key innovation lies in using a lightweight tracker as a query for a transformer-based decoder to retrieve relevant spatial features from the encoded representation while leveraging temporal information, including 2D velocity beyond range velocity. This design enables the decoder to align the orientation and velocity of the object, resulting in more accurate predictions. Furthermore, the tracker identifies points of interest, significantly reducing the number of queries processed by the decoder and improving computational efficiency. We evaluated RPT<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> on the nuScenes dataset, where it delivers outstanding performance while maintaining efficiency, demonstrating its potential as a robust solution for radar-based object detection in real-world scenarios.