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

翻译：时间前向跟踪一直是多目标分割与跟踪（MOTS）的主流方法。然而，近期针对预录制样本中芽殖酵母细胞的检测、分割与跟踪，提出了一种新颖的时间对称跟踪方法。尽管该架构已在稳定一致性跟踪及漏检实例重插值方面展现出独特视角，但其评估迄今主要局限于与视频显微环境相关的场景。本研究旨在通过多种专门设计的场景（包括行人跟踪数据集），揭示该架构更广泛的能力、优势及潜在挑战。我们同时进行了消融实验，将该模型与其受限变体及广泛使用的卡尔曼滤波器进行对比。此外，我们还对预训练与非预训练模型的跟踪架构进行了注意力分析。