IMM-MOT: 相互作用多重モデルフィルタを用いた3次元多物体追跡フレームワーク
IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter
物体の複雑な運動パターンに対応するため相互作用多重モデルフィルタを導入し、軌道管理と検出スコア補正を改善した3D多物体追跡手法を提案。NuScenesでAMOTA 73.8%を達成。
著者: Xiaohong Liu, Xulong Zhao, Gang Liu, Zili Wu, Tao Wang, Lei Meng, Yuhan Wang
分類: cs.CV, cs.RO
原文アブストラクト
3D Multi-Object Tracking (MOT) provides the trajectories of surrounding objects, assisting robots or vehicles in smarter path planning and obstacle avoidance. Existing 3D MOT methods based on the Tracking-by-Detection framework typically use a single motion model to track an object throughout its entire tracking process. However, objects may change their motion patterns due to variations in the surrounding environment. In this paper, we introduce the Interacting Multiple Model filter in IMM-MOT, which accurately fits the complex motion patterns of individual objects, overcoming the limitation of single-model tracking in existing approaches. In addition, we incorporate a Damping Window mechanism into the trajectory lifecycle management, leveraging the continuous association status of trajectories to control their creation and termination, reducing the occurrence of overlooked low-confidence true targets. Furthermore, we propose the Distance-Based Score Enhancement module, which enhances the differentiation between false positives and true positives by adjusting detection scores, thereby improving the effectiveness of the Score Filter. On the NuScenes Val dataset, IMM-MOT outperforms most other single-modal models using 3D point clouds, achieving an AMOTA of 73.8%. Our project is available at https://github.com/Ap01lo/IMM-MOT.
