Spb3DTracker: ノイズ環境に強いLiDAR人物追跡
Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment
LiDARを用いた人物検出・追跡の各構成要素を改良し、雑音の多い環境でも頑健に動作する追跡手法SpbTrackを提案した。
著者: Eunsoo Im, Changhyun Jee, Jung Kwon Lee
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Person detection and tracking (PDT) has seen significant advancements with 2D camera-based systems in the autonomous vehicle field, leading to widespread adoption of these algorithms. However, growing privacy concerns have recently emerged as a major issue, prompting a shift towards LiDAR-based PDT as a viable alternative. Within this domain, "Tracking-by-Detection" (TBD) has become a prominent methodology. Despite its effectiveness, LiDAR-based PDT has not yet achieved the same level of performance as camera-based PDT. This paper examines key components of the LiDAR-based PDT framework, including detection post-processing, data association, motion modeling, and lifecycle management. Building upon these insights, we introduce SpbTrack, a robust person tracker designed for diverse environments. Our method achieves superior performance on noisy datasets and state-of-the-art results on KITTI Dataset benchmarks and custom office indoor dataset among LiDAR-based trackers.
