用多模型动态追踪复杂运动物体，提升3D目标跟踪精度
IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter
- 引入交互多模型滤波，动态适应物体运动模式变化
- 在NuScenes数据集上达73.8%的AMOTA，优于多数单模型方法
- 适合自动驾驶、机器人感知等需要精准轨迹预测的场景
3D多目标跟踪（MOT）为机器人或车辆提供周围物体的运动轨迹，支持更智能的路径规划与避障。现有基于检测跟踪框架的方法通常使用单一运动模型全程跟踪目标，但环境变化会导致物体运动模式改变。本文提出IMM-MOT框架，引入交互多模型滤波（IMM），能准确拟合个体目标的复杂运动模式，突破单模型跟踪的局限性。同时，设计阻尼窗口机制，利用轨迹关联状态控制其创建与终止，减少低置信度真实目标被遗漏的情况。此外，提出基于距离的得分增强模块，通过调整检测分数提升假阳性和真阳性之间的区分度，增强评分过滤器效果。在NuScenes验证集上，IMM-MOT以73.8%的AMOTA超越多数基于3D点云的单模态模型。项目开源地址：https://github.com/Ap01lo/IMM-MOT。
原文摘要 · Abstract (English)
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.
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