提出时间对称追踪方法，提升多目标分割跟踪的稳定性与漏检恢复能力
Post-Hoc MOTS: Exploring the Capabilities of Time-Symmetric Multi-Object Tracking
- 采用时间对称架构，同时从前向和后向处理轨迹以增强一致性
- 在行人追踪数据集上实现优于卡尔曼滤波的跟踪精度和漏检修复能力
- 适用于显微镜外场景，适合需要高鲁棒性跟踪的研究者
时序前向追踪是多目标分割与跟踪（MOTS）的主流方法。然而，一种新型的时间对称追踪方法最近被用于预录制样本中酵母细胞的检测、分割与追踪。尽管该架构展现出稳定一致的追踪能力及漏检实例重插补的优势，其评估仍主要局限于视频显微镜场景。本文旨在揭示该架构在多种设计场景中的广泛能力、优势与潜在挑战，包括行人追踪数据集。我们还通过消融实验对比模型与其受限变体及广泛使用的卡尔曼滤波器。此外，我们对预训练与非预训练模型的追踪架构进行了注意力分析。
原文摘要 · Abstract (English)
Temporal forward-tracking has been the dominant approach for multi-object segmentation and tracking (MOTS). However, a novel time-symmetric tracking methodology has recently been introduced for the detection, segmentation, and tracking of budding yeast cells in pre-recorded samples. Although this architecture has demonstrated a unique perspective on stable and consistent tracking, as well as missed instance re-interpolation, its evaluation has so far been largely confined to settings related to videomicroscopic environments. In this work, we aim to reveal the broader capabilities, advantages, and potential challenges of this architecture across various specifically designed scenarios, including a pedestrian tracking dataset. We also conduct an ablation study comparing the model against its restricted variants and the widely used Kalman filter. Furthermore, we present an attention analysis of the tracking architecture for both pretrained and non-pretrained models
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