优化多相机3D检测跟踪，提升低帧率下的目标身份稳定性。
Model Optimization for Multi-Camera 3D Detection and Tracking
- 采用查询驱动的时空融合框架，通过实例记忆传播稀疏目标查询。
- 2帧以下时身份关联崩溃，但检测仍稳定；骨干与颈部可量化以平衡速度与精度。
- 适合需要低延迟、高鲁棒性多相机系统的研发人员参考。
室外-内多相机感知在室内场景中日益重要，需在遮挡和异构视角下实现多目标跟踪。本文评估了基于查询的时空3D检测与跟踪框架Sparse4D，该框架在共享世界坐标系中融合多视角特征，并通过实例记忆传播稀疏物体查询。研究涵盖降低输入帧率（如降至2 FPS以下）、训练后量化（INT8与FP8）、迁移至WILDTRACK基准，以及使用Transformer Engine进行混合精度微调。为更好衡量身份一致性，引入平均追踪时长（AvgTrackDur，单位：秒）。结果显示，当帧率低于2 FPS时，尽管检测稳定，身份关联仍会崩溃；选择性量化骨干与颈部模块可实现最优速度-精度权衡，而注意力模块对低精度敏感。在WILDTRACK上，低帧率预训练带来显著零样本提升，小规模微调收益有限。混合精度虽降低延迟并提升摄像机扩展性，但可能破坏身份传播，需引入稳定性感知验证。
原文摘要 · Abstract (English)
Outside-in multi-camera perception is increasingly important in indoor environments, where networks of static cameras must support multi-target tracking under occlusion and heterogeneous viewpoints. We evaluate Sparse4D, a query-based spatiotemporal 3D detection and tracking framework that fuses multi-view features in a shared world frame and propagates sparse object queries via instance memory. We study reduced input frame rates, post-training quantization (INT8 and FP8), transfer to the WILDTRACK benchmark, and Transformer Engine mixed-precision fine-tuning. To better capture identity stability, we report Average Track Duration (AvgTrackDur), which measures identity persistence in seconds. Sparse4D remains stable under moderate FPS reductions, but below 2 FPS, identity association collapses even when detections are stable. Selective quantization of the backbone and neck offers the best speed-accuracy trade-off, while attention-related modules are consistently sensitive to low precision. On WILDTRACK, low-FPS pretraining yields large zero-shot gains over the base checkpoint, while small-scale fine-tuning provides limited additional benefit. Transformer Engine mixed precision reduces latency and improves camera scalability, but can destabilize identity propagation, motivating stability-aware validation.
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