https://arxiv.org/pdf/2310.17659v1.pdf 이번에 리뷰할 논문은 기존 RTNH의 성능을 높인 RTNH+이다. 4D Radar dataset인 K-Radar를 대상으로 3D object detection에 대한 모델이다. 배경 4D Radar는 3D object detection을 수행하는데 주변 날씨에 강건하다는 장점을 가진다. 하지만, Radar 측정값은 noise, interference, and clutter와 같은 요소들에 의해 corrupt되기 때문에 preprocessing 과정이 필요하다. 본 논문에서는 두 가지 알고리즘을 소개한다. Combined Constant false alarm rate (CFAR)-based Two-level Preprocessing (C..
https://arxiv.org/abs/2108.10312 Exploring Simple 3D Multi-Object Tracking for Autonomous Driving 3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a heuristic matching step for the detection association. In arxiv.org 이번에 review할 논문은 3D MOT 논문 중 Joint ..
https://arxiv.org/abs/2309.06036 Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar? Online 3D multi-object tracking (MOT) has recently received significant research interests due to the expanding demand of 3D perception in advanced driver assistance systems (ADAS) and autonomous driving (AD). Among the existing 3D MOT framewor..
https://arxiv.org/abs/1909.12605 Towards Real-Time Multi-Object Tracking Modern multiple object tracking (MOT) systems usually follow the \emph{tracking-by-detection} paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed arxiv.org Introduction 현대의 MOT system은 주로 tracking-by-detection 방식을 ..
