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Tracking through Containers and Occluders in the Wild

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Basile Van Hoorick, Pavel Tokmakov, Simon Stent, Jie Li, Carl Vondrick

Computer Vision and Pattern Recognition · Submitted 4 May 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We evaluate two recent transformer-based video models and find that while they can be surprisingly capable of tracking targets under certain settings of task variation, there remains a considerable performance gap before we can claim a tracking model to have acquired a true notion of object permanence.

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arXiv ID2305.03052 (version 1)
Pith IDRDAO7H6F, Pith's permanent record of this paper and its review
CitationBasile Van Hoorick, Carl Vondrick, Jie Li, Pavel Tokmakov, Simon Stent. "Tracking through Containers and Occluders in the Wild." arXiv:2305.03052 (2023). https://pith.science/paper/RDAO7H6F
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,341 input tokens.

What the abstract itself states, as probabilities: claims first 0.15 · states limitation 0.64 · human participants 0.05 · controlled comparison 0.30 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.88 · mentions code or data availability 0.07.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce $\textbf{TCOW}$, a new benchmark and model for visual tracking through heavy occlusion and containment. We set up a task where the goal is to, given a video sequence, segment both the projected extent of the target object, as well as the surrounding container or occluder whenever one exists. To study this task, we create a mixture of synthetic and annotated real datasets to support both supervised learning and structured evaluation of model performance under various forms of task variation, such as moving or nested containment. We evaluate two recent transformer-based video models and find that while they can be surprisingly capable of tracking targets under certain settings of task variation, there remains a considerable performance gap before we can claim a tracking model to have acquired a true notion of object permanence.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. background 0.99 0.04 0.03 asserted 1.00
s1 In this paper, we introduce $\textbf{TCOW}$, a new benchmark and model for visual tracking through heavy occlusion and containment. contribution 1.00 0.93 0.06 asserted 0.99
s2 We set up a task where the goal is to, given a video sequence, segment both the projected extent of the target object, as well as the surrounding container or occluder whenever one exists. method 0.54 0.82 0.03 asserted 0.88
s3 To study this task, we create a mixture of synthetic and annotated real datasets to support both supervised learning and structured evaluation of model performance under various forms of task variation, such as moving or nested containment. method 0.94 0.96 0.03 asserted 0.99
s4 We evaluate two recent transformer-based video models and find that while they can be surprisingly capable of tracking targets under certain settings of task variation, there remains a considerable performance gap before we can claim a tracking model to have acquired a true notion of object permanence. result 0.82 0.96 0.75 asserted 1.00

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.97.

What was examined, and what was not

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  • references and citing sentences
  • relations to other papers
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce $\textbf{TCOW}$, a new benchmark and model for visual tracking through heavy occlusion and containment. We set up a task where the goal is to, given a video sequence, segment both the projected extent of the target object, as well as the surrounding container or occluder whenever one exists. To study this task, we create a mixture of synthetic and annotated real datasets to support both supervised learning and structured evaluation of model performance under various forms of task variation, such as moving or nested containment. We evaluate two recent transformer-based video models and find that while they can be surprisingly capable of tracking targets under certain settings of task variation, there remains a considerable performance gap before we can claim a tracking model to have acquired a true notion of object permanence.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 2 sentences without a confident label

Builds on 1 paper
  • In this work, we extend the traditional VOS setting to include segmenting (the occluders and containers of) fully invisible objects, as well as amodally completing partially visible ones [ref] .

    A Tri-Layer Plugin to Improve Occluded Detection · Related Work · 0.89

Uses the method or tool of 1 paper
Uses data or values from 2 papers
Cites as background 2 papers
  • Object permanence methods in computer vision were mostly studied in the context of multi-object tracking - the task of localizing all the objects from a pre-defined vocabulary with bounding boxes and associating them over time based on identity [ref] .

    MOT16: A Benchmark for Multi-Object Tracking · Related Work · 0.97

  • Promising synthetic generators and datasets have been proposed to support various tasks in different domains, including CARLA [ref] and ParallelDomain [ref] for scene analysis and behavior understanding in autonomous driving, Flying Chairs [ref] and Sintel [ref] for optical flow, and ThreeDWorld [ref] and Kubric [ref] for a wide variety of perception tasks in general scenes.

    ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation · Related Work · 0.81

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