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

• URL: http://arxiv.org/abs/2412.08313v1
• Date: Wed, 11 Dec 2024 11:50:06 GMT
• Title: Post-Hoc MOTS: Exploring the Capabilities of Time-Symmetric Multi-Object Tracking
• Authors: Gergely Szabó, Zsófia Molnár, András Horváth,
• Abstract summary: A time-symmetric tracking methodology has been introduced for the detection, segmentation, and tracking of budding yeast cells in pre-recorded samples. We aim to reveal the broader capabilities, advantages, and potential challenges of this architecture across various specifically designed scenarios. We present an attention analysis of the tracking architecture for both pretrained and non-pretrained models.
• Score: 0.37240490024629924
• License:
• 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

Related papers

• Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking [53.33637391723555]
  We propose a unified multimodal spatial-temporal tracking approach named STTrack. In contrast to previous paradigms, we introduced a temporal state generator (TSG) that continuously generates a sequence of tokens containing multimodal temporal information. These temporal information tokens are used to guide the localization of the target in the next time state, establish long-range contextual relationships between video frames, and capture the temporal trajectory of the target.
  arXiv  Detail & Related papers  (2024-12-20T09:10:17Z)
• Multi-step Temporal Modeling for UAV Tracking [14.687636301587045]
  We introduce MT-Track, a streamlined and efficient multi-step temporal modeling framework for enhanced UAV tracking. We unveil a unique temporal correlation module that dynamically assesses the interplay between the template and search region features. We propose a mutual transformer module to refine the correlation maps of historical and current frames by modeling the temporal knowledge in the tracking sequence.
  arXiv  Detail & Related papers  (2024-03-07T09:48:13Z)
• MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking [56.92165669843006]
  We propose MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range. For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target. For extreme occlusions, we build a novel Refind Module to learn reliable long-term motions from the target's history trajectory, which can link the interrupted trajectory with its corresponding detection.
  arXiv  Detail & Related papers  (2023-03-18T12:38:33Z)
• Spatiotemporal k-means [39.98633724527769]
  We propose a twotemporal clustering method called k-means (STk) that is able to analyze multi-scale clusters. We show how STkM can be extended to more complex machine learning tasks, particularly unsupervised region of interest detection and tracking in videos.
  arXiv  Detail & Related papers  (2022-11-10T04:40:31Z)
• End-to-end Tracking with a Multi-query Transformer [96.13468602635082]
  Multiple-object tracking (MOT) is a challenging task that requires simultaneous reasoning about location, appearance, and identity of the objects in the scene over time. Our aim in this paper is to move beyond tracking-by-detection approaches, to class-agnostic tracking that performs well also for unknown object classes.
  arXiv  Detail & Related papers  (2022-10-26T10:19:37Z)
• MONCE Tracking Metrics: a comprehensive quantitative performance evaluation methodology for object tracking [0.0]
  We propose a suite of MONCE (Multi-Object Non-Contiguous Entities) image tracking metrics that provide both objective tracking model performance benchmarks as well as diagnostic insight for driving tracking model development.
  arXiv  Detail & Related papers  (2022-04-11T17:32:03Z)
• Multi-Object Tracking and Segmentation with a Space-Time Memory Network [12.043574473965318]
  We propose a method for multi-object tracking and segmentation based on a novel memory-based mechanism to associate tracklets. The proposed tracker, MeNToS, addresses particularly the long-term data association problem.
  arXiv  Detail & Related papers  (2021-10-21T17:13:17Z)
• Learning to Track with Object Permanence [61.36492084090744]
  We introduce an end-to-end trainable approach for joint object detection and tracking. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets.
  arXiv  Detail & Related papers  (2021-03-26T04:43:04Z)
• Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking [83.75789829291475]
  We introduce a probabilistic autoregressive motion model to score tracklet proposals. This is achieved by training our model to learn the underlying distribution of natural tracklets. Our experiments demonstrate the superiority of our approach at tracking objects in challenging sequences.
  arXiv  Detail & Related papers  (2020-12-03T23:59:27Z)
• ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking [80.02322563402758]
  One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets. We introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion.
  arXiv  Detail & Related papers  (2020-04-16T06:43:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.