Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs

• URL: http://arxiv.org/abs/2409.17221v1
• Date: Wed, 25 Sep 2024 18:00:00 GMT
• Title: Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs
• Authors: Mattia Segu, Luigi Piccinelli, Siyuan Li, Luc Van Gool, Fisher Yu, Bernt Schiele,
• Abstract summary: We introduce Walker, the first self-supervised tracker that learns from videos with sparse bounding box annotations, and no tracking labels. Walker is the first self-supervised tracker to achieve competitive performance on MOT17, DanceTrack, and BDD100K.
• Score: 117.67620297750685
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The supervision of state-of-the-art multiple object tracking (MOT) methods requires enormous annotation efforts to provide bounding boxes for all frames of all videos, and instance IDs to associate them through time. To this end, we introduce Walker, the first self-supervised tracker that learns from videos with sparse bounding box annotations, and no tracking labels. First, we design a quasi-dense temporal object appearance graph, and propose a novel multi-positive contrastive objective to optimize random walks on the graph and learn instance similarities. Then, we introduce an algorithm to enforce mutually-exclusive connective properties across instances in the graph, optimizing the learned topology for MOT. At inference time, we propose to associate detected instances to tracklets based on the max-likelihood transition state under motion-constrained bi-directional walks. Walker is the first self-supervised tracker to achieve competitive performance on MOT17, DanceTrack, and BDD100K. Remarkably, our proposal outperforms the previous self-supervised trackers even when drastically reducing the annotation requirements by up to 400x.

Related papers

• OmniTracker: Unifying Object Tracking by Tracking-with-Detection [119.51012668709502]
  OmniTracker is presented to resolve all the tracking tasks with a fully shared network architecture, model weights, and inference pipeline. Experiments on 7 tracking datasets, including LaSOT, TrackingNet, DAVIS16-17, MOT17, MOTS20, and YTVIS19, demonstrate that OmniTracker achieves on-par or even better results than both task-specific and unified tracking models.
  arXiv  Detail & Related papers  (2023-03-21T17:59:57Z)
• An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds [50.19288542498838]
  3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. We introduce a motion-centric paradigm to handle LiDAR SOT from a new perspective.
  arXiv  Detail & Related papers  (2023-03-21T17:28:44Z)
• Tracking by Associating Clips [110.08925274049409]
  In this paper, we investigate an alternative by treating object association as clip-wise matching. Our new perspective views a single long video sequence as multiple short clips, and then the tracking is performed both within and between the clips. The benefits of this new approach are two folds. First, our method is robust to tracking error accumulation or propagation, as the video chunking allows bypassing the interrupted frames. Second, the multiple frame information is aggregated during the clip-wise matching, resulting in a more accurate long-range track association than the current frame-wise matching.
  arXiv  Detail & Related papers  (2022-12-20T10:33:17Z)
• Simple Cues Lead to a Strong Multi-Object Tracker [3.7189423451031356]
  We propose a new type of tracking-by-detection (TbD) for Multi-Object Tracking. We show that a combination of our appearance features with a simple motion model leads to strong tracking results. Our tracker generalizes to four public datasets, namely MOT17, MOT20, BDD100k, and DanceTrack, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-06-09T17:55:51Z)
• Learning to Track Objects from Unlabeled Videos [63.149201681380305]
  In this paper, we propose to learn an Unsupervised Single Object Tracker (USOT) from scratch. To narrow the gap between unsupervised trackers and supervised counterparts, we propose an effective unsupervised learning approach composed of three stages. Experiments show that the proposed USOT learned from unlabeled videos performs well over the state-of-the-art unsupervised trackers by large margins.
  arXiv  Detail & Related papers  (2021-08-28T22:10:06Z)
• Unsupervised Deep Representation Learning for Real-Time Tracking [137.69689503237893]
  We propose an unsupervised learning method for visual tracking. The motivation of our unsupervised learning is that a robust tracker should be effective in bidirectional tracking. We build our framework on a Siamese correlation filter network, and propose a multi-frame validation scheme and a cost-sensitive loss to facilitate unsupervised learning.
  arXiv  Detail & Related papers  (2020-07-22T08:23:12Z)
• Unsupervised Multiple Person Tracking using AutoEncoder-Based Lifted Multicuts [11.72025865314187]
  We present an unsupervised multiple object tracking approach based on minimum visual features and lifted multicuts. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
  arXiv  Detail & Related papers  (2020-02-04T09:42:34Z)

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.