Recent 3D multi-object tracking (3D MOT) methods mainly follow tracking-by-detection pipelines, but often suffer from high false positives, missed detections, and identity switches, especially in crowded and small-object scenarios. To address these challenges, we propose Easy-Poly, a filter-based 3D MOT framework with four key innovations: (1) CNMSMM, a novel Camera-LiDAR fusion detection method combining multi-modal augmentation and an efficient NMS with a new loss function to improve small target detection; (2) Dynamic Track-Oriented (DTO) data association that robustly handles uncertainties and occlusions via class-aware optimal assignment and parallel processing strategies; (3) Dynamic Motion Modeling (DMM) using a confidence-weighted Kalman filter with adaptive noise covariance to enhance tracking accuracy; and (4) an extended life-cycle management system reducing identity switches and false terminations. Experimental results show that Easy-Poly outperforms state-of-the-art methods such as Poly-MOT and Fast-Poly, achieving notable gains in mAP (e.g., from 63.30% to 65.65% with LargeKernel3D) and AMOTA (e.g., from 73.1% to 75.6%), while also running in real-time. Our framework advances robustness and adaptability in complex driving environments, paving the way for safer autonomous driving perception.

翻译：当前的三维多目标跟踪方法主要遵循检测跟踪范式，但在拥挤场景和小目标场景中常面临高误报率、漏检和身份切换等问题。为应对这些挑战，本文提出Easy-Poly——一种基于滤波器的三维多目标跟踪框架，其包含四项核心创新：(1) CNMSMM：一种新颖的相机-激光雷达融合检测方法，结合多模态增强技术与采用新型损失函数的高效非极大值抑制，以提升小目标检测性能；(2) 动态轨迹导向数据关联：通过类别感知最优分配与并行处理策略，鲁棒处理目标不确定性与遮挡问题；(3) 动态运动建模：采用带自适应噪声协方差的置信度加权卡尔曼滤波器以提升跟踪精度；(4) 扩展生命周期管理系统：有效减少身份切换与错误终止。实验结果表明，Easy-Poly在多项指标上超越Poly-MOT、Fast-Poly等先进方法，mAP显著提升（如使用LargeKernel3D时从63.30%提升至65.65%），AMOTA指标同步改善（如从73.1%提升至75.6%），同时保持实时运行性能。本框架增强了复杂驾驶环境下的鲁棒性与适应性，为提升自动驾驶感知安全性提供了新路径。