自律走行のための確率的3Dマルチモーダル多物体追跡
Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
2D画像と3D LiDARを融合して物体の外観・形状特徴を学習し、データ関連付けとトラック初期化を学習ベースで行うことで、自律走行向け多物体追跡の精度を向上させた研究。
著者: Hsu-kuang Chiu, Jie Li, Rares Ambrus, Jeannette Bohg
分類: cs.CV, cs.RO
原文アブストラクト
Multi-object tracking is an important ability for an autonomous vehicle to safely navigate a traffic scene. Current state-of-the-art follows the tracking-by-detection paradigm where existing tracks are associated with detected objects through some distance metric. The key challenges to increase tracking accuracy lie in data association and track life cycle management. We propose a probabilistic, multi-modal, multi-object tracking system consisting of different trainable modules to provide robust and data-driven tracking results. First, we learn how to fuse features from 2D images and 3D LiDAR point clouds to capture the appearance and geometric information of an object. Second, we propose to learn a metric that combines the Mahalanobis and feature distances when comparing a track and a new detection in data association. And third, we propose to learn when to initialize a track from an unmatched object detection. Through extensive quantitative and qualitative results, we show that when using the same object detectors our method outperforms state-of-the-art approaches on the NuScenes and KITTI datasets.
