Abstract
We propose FutrTrack, a modular camera-LiDAR multi-object tracking framework that builds on existing 3D detectors by introducing a transformer-based smoother and a fusion-driven tracker. Inspired by query-based tracking frameworks, FutrTrack employs a multimodal two-stage transformer refinement and tracking pipeline. Our fusion tracker integrates bounding boxes with multimodal bird's-eye-view (BEV) fusion features from multiple cameras and LiDAR without the need for an explicit motion model. The tracker assigns and propagates identities across frames, leveraging both geometric and semantic cues for robust re-identification under occlusion and viewpoint changes. Prior to tracking, we refine sequences of bounding boxes with a temporal smoother over a moving window to refine trajectories, reduce jitter, and improve spatial consistency. Evaluated on nuScenes and KITTI, FutrTrack demonstrates that query-based transformer tracking methods benefit significantly from multimodal sensor features compared with previous single-sensor approaches. With an aMOTA of 74.7 on the nuScenes test set, FutrTrack achieves strong performance on 3D MOT benchmarks, reducing identity switches while maintaining competitive accuracy. Our approach provides an efficient framework for improving transformer-based trackers to compete with other neural-network-based methods even with limited data and without pretraining.
Abstract (translated)
我们提出了一种名为FutrTrack的模块化相机-激光雷达多目标跟踪框架，该框架在现有的3D检测器基础上引入了基于变换器的平滑器和融合驱动的跟踪器。受查询基元跟踪框架的启发，FutrTrack采用了一个跨模态两阶段变换器细化和跟踪流水线。我们的融合跟踪器将边界框与来自多个相机和激光雷达的多模态鸟瞰图（BEV）融合特征相结合，并不需要明确的运动模型。该跟踪器可以在帧之间分配并传播身份信息，利用几何和语义线索，在遮挡和视角变化的情况下实现稳健的重新识别。在进行追踪之前，我们通过在一个移动窗口上使用时序平滑器来细化边界框序列，以优化轨迹、减少抖动，并提高空间一致性。 在nuScenes和KITTI数据集上的评估表明，查询基元变换器跟踪方法从多模态传感器特征中受益匪浅，相较于之前的单传感器方法。FutrTrack在nuScenes测试集中达到了74.7的aMOTA（平均多目标跟踪准确率），展示了其在3D MOT基准测试中的强劲性能，在减少身份切换的同时保持了竞争力的精度。 我们的方法提供了一个高效的框架，用于改进基于变换器的追踪器，使其能够在数据有限且无预训练的情况下与其它神经网络方法竞争。
URL
https://arxiv.org/abs/2510.19981
