Post-Hoc MOTS: Exploring the Capabilities of Time-Symmetric Multi-Object Tracking

TL;DR

This paper investigates the broader applicability and advantages of a novel time-symmetric multi-object tracking architecture beyond microscopic environments, including pedestrian datasets, through extensive evaluation and ablation studies.

Contribution

It extends the evaluation of time-symmetric MOTS to diverse scenarios, compares it with Kalman filter, and analyzes attention mechanisms for both pretrained and non-pretrained models.

Findings

Time-symmetric MOTS performs well across various scenarios.

The architecture offers stable and consistent tracking advantages.

Attention analysis reveals insights into model behavior.

Abstract

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