TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking

Christian Heiliger; Lennart Bastian; Nassir Navab; Tony Danjun Wang

REVIEW 3 major objections 3 minor 31 references

TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking

T0 review · 3 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read TrackOR claims that 3D geometric signatures alone can preserve each staff member's identity in the operating room across long absences, beating appearance-based tracking by 11% Association Accuracy.

desk verdict The idea of geometry-based ReID for OR staff is motivated and plausible, but the provided text hides the method and all experimental support, leaving the +11% AssA headline as an unverifiable assertion. read the letter →

arxiv 2508.07968 v1 pith:ZBVN223J submitted 2025-08-11 cs.CV

Tony Danjun Wang , Christian Heiliger , Nassir Navab , Lennart Bastian This is my paper · ORCID

classification cs.CV

keywords surgicaldatasciencemulti-objecttrackingpersonre-identification3Dgeometricsignaturesoperatingroomlong-termtemporalpathwayimprints

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

TrackOR argues that in the visually homogeneous operating room, identity should come from each person's 3D body geometry rather than from appearance, because scrubs and gowns make people look alike. The paper introduces a tracking framework that builds a view-invariant geometric signature for each person from multi-view RGB-D point clouds, uses it for online association, and adds an offline recovery stage that reconnects tracklets after staff leave and re-enter the room. On OR data, the method reports a +11% gain in Association Accuracy over the strongest baseline. If this holds, long-running staff-centric trajectories become available for workflow analysis, safety monitoring, and personalized support. The same signature also powers temporal pathway imprints, a proposed visualization of where each staff member spends time.

What carries the argument

The central object is the 3D geometric signature: a per-person representation computed from multi-view RGB-D point clouds and 3D poses, designed to be invariant to camera viewpoint and decoupled from visual and textural appearance. It carries the argument because the same signature is used both for online association of detections and for offline re-identification after absence, so short-term tracking and long-term identity recovery rest on one mechanism.

What would settle it

A targeted stress test would run TrackOR on sequences where two staff members of similar stature wear the same loose gown, stand in similar poses near the table, and one leaves and re-enters. If re-identification accuracy collapses to chance under those conditions, then the geometric signature is not stable or discriminative enough for the paper's central claim. A direct measurement of within-person versus between-person signature distances under partial point clouds would also settle the question.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that a person's 3D geometry—the shape of the body as seen through multi-view point clouds, not the color or texture of clothing—carries enough identity information to keep a stable ID through the long, occluded, revolving-door conditions of a real OR. TrackOR extracts this view-invariant geometric signature, associates people online while they are visible, and, when someone leaves and returns, uses the signature to reconnect the broken track in an offline recovery step that yields analysis-ready trajectories. The paper reports +11% Association Accuracy over the strongest baseline and introduces temporal pathway imprints as a way to turn the recovere

Load-bearing premise

The load-bearing premise is that a person's 3D body shape, as captured by point clouds, stays similar enough across time and viewpoints, and different enough between people, to act as an identity even when staff wear loose, identical scrubs and are only partially visible.

Editorial extensions

If this is right

Tracking can survive the revolving-door reality of surgery: staff who leave and re-enter keep the same ID, so trajectory analysis no longer breaks at the door.
Workflow analysis can shift from role-level archetypes, such as 'the circulating nurse', to individual staff-centric histories over long procedures.
Temporal pathway imprints convert raw tracking data into actionable summaries of where each person spends time, supporting team efficiency and safety review.
Appearance-based ReID becomes much less relevant in the OR, since identity is carried by geometry rather than by visually indistinct attire.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper: if 3D geometry alone is identity-discriminative, the same signature could support privacy-preserving OR analytics that avoid storing face or body-appearance images.
Beyond the paper: the approach may transfer to other uniformed team environments—emergency rooms, cleanrooms, industrial floors—where appearance is homogeneous but 3D sensors are available; this is a testable extension, not a claim in the paper.
Beyond the paper: temporal pathway imprints could be used predictively, so that a deviation from a staff member's usual spatial pattern flags a workflow disruption or safety event before it becomes critical.
Beyond the paper: the offline recovery stage suggests a latency-accuracy trade-off that could be pushed into real time with a delayed-association variant, letting the system bridge absences without waiting for the end of the procedure.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The thing to know up front: the version we're looking at is an abstract-plus-intro-plus-conclusion artifact, so the central empirical claim is a number floating in the air. The +11% Association Accuracy over the 'strongest baseline' is stated twice but no baseline is named, no table is shown, no ablation separates online gains from offline recovery, and there is no error bar. If the full paper contains the missing Sections 2–4, then my concerns are probably addressed; as supplied, the headline result is an assertion.

What is genuinely good: the problem framing is right. Homogeneous scrubs, the revolving-door of staff leaving and re-entering, and the failure of appearance-based ReID are real, and the proposal to use 3D geometric signatures as a view-invariant identity cue is a sensible, non-obvious direction. Moving from role-based to staff-centric OR analysis is a well-argued motivation, and the offline trajectory recovery idea is a useful capability even if the tracking itself is solid only frame-to-frame.

The main soft spot, beyond the missing evidence, is the load-bearing assumption that the geometric signature is identity-discriminative and temporally stable under loose gowns, partial multi-view point clouds, and re-entry in different postures. The intro claims the signature is 'robust' and 'decoupled from confounding visual and textural cues,' but without a definition, invariance analysis, or re-identification experiment, this is a hope, not a result. The stress-test concern lands directly on this gap. I don't think the self-citation cluster is a red flag in itself, since the earlier work is on OR ReID and the new work extends it, but the unnamed baseline does leave room for favorable framing.

Who gets value: people working in surgical data science, multi-object tracking, and OR workflow analysis. If the full paper ships with real experiments, this could be a solid contribution; the idea is worth engaging with. As it stands, I would not cite the +11% claim, though I might cite the problem formulation.

My recommendation: yes, send it to peer review. The idea is promising enough that the missing sections deserve a fair look from referees, who should ask for the method details, named baselines, and an ablation of the geometric signature's stability under partial observation. That's a potentially fixable gap, not a dead end.

Referee Report

3 major / 3 minor

Summary. The paper proposes TrackOR, a framework for long-term multi-person tracking and re-identification in the operating room (OR). The central claim, stated in the abstract and Section 1, is that 3D geometric signatures provide a view-invariant, identity-discriminative representation that yields state-of-the-art online tracking performance (+11% Association Accuracy over the strongest baseline) and enables offline recovery of analysis-ready trajectories. The paper further introduces 'temporal pathway imprints' as an application of persistent identity tracking. However, the submitted text contains only the abstract, Section 1, a partial figure, Section 5 (Conclusion), and references; the method and experiments sections are absent. Consequently, the core technical claims—the definition of the geometric signature, the tracking and re-identification algorithm, the evaluation protocol, and the supporting empirical evidence—are not present in the manuscript under review.

Significance. If the claims are correct, the work would be a meaningful step for surgical data science: persistent identity tracking in the OR would move analyses from role-level to staff-level, enabling longitudinal workflow and safety studies. The proposed shift from appearance-based ReID to 3D geometric signatures is plausible and worth investigating, especially because OR staff wear homogeneous attire. However, significance is entirely contingent on evidence that the geometric signature is stable under real OR conditions, which the current manuscript does not provide. The paper also contains no machine-checked proofs, reproducible experiments, or parameter-free derivations; the only concrete promise is future public code. As submitted, the contribution is an extended abstract rather than a complete, verifiable research paper.

major comments (3)

[Abstract and Section 1] The central empirical claim—'+11% Association Accuracy over the strongest baseline'—is stated without any supporting table, experiment, named baseline, dataset, or evaluation protocol. The text skips the experiments section entirely. This is load-bearing: the entire contribution rests on this quantitative claim. The authors must provide a full experimental section with dataset description, metric definitions (e.g., HOTA/AssA), comparison methods, error bars, and ablations that separate the online tracking gain from the offline recovery gain.
[Section 1] The load-bearing premise is that identity is 'derived from a robust, view-invariant signature that is decoupled from confounding visual and textural cues.' No definition, mathematical formulation, or extraction procedure is given for this geometric signature. It is therefore impossible to assess whether it is identity-discriminative and temporally stable under the OR conditions the paper itself lists: loose gowns that alter body surface, severe occlusions, crowded scenes around the table, and fragmentary multi-view point clouds. The manuscript must define the signature and provide invariance analysis or targeted experiments (e.g., re-identification across re-entry with changed posture/clothing, partial point-cloud ablations).
[Section 1 / Section 5] The 'offline recovery process' and 'temporal pathway imprints' are mentioned but never specified. The paper does not state the temporal window, gap-filling parameters, matching thresholds, or how offline recovery differs from online association. Without these details, the claim of 'analysis-ready trajectories' is unverifiable. Either include the full method and evaluation, or clearly delineate these as future work rather than presented results.

minor comments (3)

[Section 1] Typographical issues: 'revolving door4‘' has a misplaced superscript and quote; 'ofwho' is missing a space; 'TrackORuses' is missing a space. These should be corrected.
[General] The manuscript contains the placeholder text 'Title Suppressed Due to Excessive Length' and jumps directly from Section 1 to the conclusion. While the missing sections are the main issue, the formatting should be cleaned so that section numbering is continuous and no placeholder text remains.
[References] The 'strongest baseline' is unnamed. If the intended comparison is against a particular published method, that method should be explicitly named in the abstract or introduction. This is more than a typo; it is part of the unsupported claim and should be fixed together with the experimental section.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detected: the paper contains no derivation chain where an output reduces to an input; self-citations are motivational and externally falsifiable.

full rationale

The available text provides no equations, no fitting procedure, no ablation, and no explicit baseline definition, so there is no derivational chain in which a prediction is equivalent to an input by construction. The central claim—that 3D geometric signatures enable long-term re-identification and improve Association Accuracy by 11%—is an empirical system claim whose evaluation is not described in the visible text; that is an evidential gap, not circularity. The only self-citation used in the argument is [29], cited in the Introduction for the contextual premise that appearance-based ReID is challenged by the visual homogeneity of the OR (e.g., 'such methods are bound to fail when confronted with the visual homogeneity of the OR [29]'). This is a published, externally falsifiable prior result from the same authors, and it is not used to define the geometric signature, to prove uniqueness, or to derive the reported numeric improvement. No ansatz is imported via citation, and no known result is merely renamed with a new coordinate system. Therefore no circular step can be exhibited under the stated criteria, and the appropriate score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The central claim rests on two unverified domain assumptions: that multi-view point clouds supply stable geometry in a crowded OR, and that 3D geometry is discriminative enough to serve as a persistent identity signature under baggy, homogeneous attire. The rest is standard tracking machinery (Kalman/Hungarian). The only invented artifact, temporal pathway imprints, is an unevaluated application proposal. All inferred parameters are flagged as such because the method section was not in the reviewed text.

free parameters (2)

3D signature matching thresholds (association gating)
A matching scheme between geometric signatures requires distance or gating cutoffs that are typically tuned; the methods section is absent from the reviewed text, so the values and whether they were fixed a priori cannot be checked. Inferred, not observed.
Offline recovery temporal window and gap-filling parameters
The offline recovery that creates analysis-ready trajectories implies choices about how long a gap can be bridged and how trajectories are stitched; no values appear in the abstract or conclusion. Inferred, not observed.

assumptions (4)

standard math Hungarian assignment and Kalman filtering as association machinery
References [13] and [14] are cited; this is standard background math, unproblematic.
domain assumption Multi-view RGB-D cameras capture enough 3D geometry of each person in the crowded OR to compute a stable signature
Required by the whole approach; occlusions and crowding (acknowledged in Section 1) threaten partial or missing geometry. Invoked implicitly by the abstract's claim that TrackOR 'uses 3D geometric signatures'.
domain assumption 3D geometry is identity-discriminative and time-stable even under homogeneous, loose OR attire and after prolonged absences
The load-bearing premise; Section 1 states identity 'is derived from a robust, view-invariant signature that is decoupled from confounding visual and textural cues'. If false, ReID via geometry fails regardless of tracking quality.
domain assumption Appearance-based ReID cannot work in the OR because staff wear visually indistinct attire
Motivation in Section 1 for abandoning appearance; plausible, but asserted rather than demonstrated in the provided text.

invented entities (1)

temporal pathway imprints
purpose: Visualization/translation of raw per-person tracking trajectories into workflow insights for team efficiency and safety
Proposed in the abstract and conclusion as an application of TrackOR outputs; no evaluation or external validation appears in the reviewed text.

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Cite this review

Pith. "Pith review of TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking." pith.science (2026). https://pith.science/paper/ZBVN223J

@misc{pith2026250807968,
  author       = {Pith},
  title        = {Pith review of: TrackOR: Towards Personalized Intelligent Operating Rooms Through Robust Tracking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZBVN223J}},
  note         = {Machine review of arXiv:2508.07968}
}

read the original abstract

Providing intelligent support to surgical teams is a key frontier in automated surgical scene understanding, with the long-term goal of improving patient outcomes. Developing personalized intelligence for all staff members requires maintaining a consistent state of who is located where for long surgical procedures, which still poses numerous computational challenges. We propose TrackOR, a framework for tackling long-term multi-person tracking and re-identification in the operating room. TrackOR uses 3D geometric signatures to achieve state-of-the-art online tracking performance (+11% Association Accuracy over the strongest baseline), while also enabling an effective offline recovery process to create analysis-ready trajectories. Our work shows that by leveraging 3D geometric information, persistent identity tracking becomes attainable, enabling a critical shift towards the more granular, staff-centric analyses required for personalized intelligent systems in the operating room. This new capability opens up various applications, including our proposed temporal pathway imprints that translate raw tracking data into actionable insights for improving team efficiency and safety and ultimately providing personalized support.

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Reference graph

Works this paper leans on

31 extracted references · 17 canonical work pages

[1]

arXiv preprint arXiv:2206.14651 (2022)

Aharon, N., Orfaig, R., Bobrovsky, B.Z.: Bot-sort: Robust associations multi- pedestrian tracking. arXiv preprint arXiv:2206.14651 (2022)

arXiv 2022
[2]

MICCAI 2022 AE-CAI Workshop11(4), 1113–1121 (2023)

Bastian, L., Czempiel, T., Heiliger, C., Karcz, K., Eck, U., Busam, B., Navab, N.: Know your sensors—a modality study for surgical action classification. MICCAI 2022 AE-CAI Workshop11(4), 1113–1121 (2023)

2022
[3]

In: MICCAI

Bastian, L., Derkacz-Bogner, D., Wang, T.D., Busam, B., Navab, N.: Segmentor: Obtaining efficient operating room semantics through temporal propagation. In: MICCAI. pp. 57–67. Springer (2023)

2023
[4]

IJCARS pp

Bastian, L., Wang, T.D., Czempiel, T., Busam, B., Navab, N.: Disguisor: holistic face anonymization for the operating room. IJCARS pp. 1–7 (2023)

2023
[5]

Machine Vision and Applications27, 1035–1046 (2016)

Belagiannis, V., Wang, X., Shitrit, H.B.B., Hashimoto, K., Stauder, R., Aoki, Y., Kranzfelder, M., Schneider, A., Fua, P., Ilic, S., et al.: Parsing human skeletons in an operating room. Machine Vision and Applications27, 1035–1046 (2016)

work page 2016
[6]

IEEE Transactions on Pattern Analysis and Machine Intelligence 33(9), 1806–1819 (2011)

Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k- shortest paths optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(9), 1806–1819 (2011)

work page 2011
[7]

EURASIP Journal on Image and Video Processing2008, 1–10 (2008)

Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP Journal on Image and Video Processing2008, 1–10 (2008)

2008
[8]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Cao, J., Pang, J., Weng, X., Khirodkar, R., Kitani, K.: Observation-centric sort: Rethinking sort for robust multi-object tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9686–9696 (2023)

2023

Show all 31 references

[9]

Neurocomputing381, 61– 88 (2020)

Ciaparrone, G., Sánchez, F.L., Tabik, S., Troiano, L., Tagliaferri, R., Herrera, F.: Deep learning in video multi-object tracking: A survey. Neurocomputing381, 61– 88 (2020)

2020
[10]

In: ECCV Workshops

Czempiel, T., Sharghi, A., Paschali, M., Navab, N., Mohareri, O.: Surgical work- flow recognition: From analysis of challenges to architectural study. In: ECCV Workshops. Springer (2022)

2022
[11]

IEEE Transactions on Multimedia25, 8725–8737 (2023)

Du, Y., Zhao, Z., Song, Y., Zhao, Y., Su, F., Gong, T., Meng, H.: Strongsort: Make deepsort great again. IEEE Transactions on Multimedia25, 8725–8737 (2023)

2023
[12]

Journal of Imaging8(8) (2022)

Hu, H., Hachiuma, R., Saito, H., Takatsume, Y., Kajita, H.: Multi-camera multi- person tracking and re-identification in an operating room. Journal of Imaging8(8) (2022)

2022
[13]

Journal of Basic Engineering82(1), 35–45 (03 1960)

Kalman, R.E.: A new approach to linear filtering and prediction problems. Journal of Basic Engineering82(1), 35–45 (03 1960)

1960
[14]

Naval research logistics quarterly 2(1-2), 83–97 (1955)

Kuhn, H.W.: The hungarian method for the assignment problem. Naval research logistics quarterly 2(1-2), 83–97 (1955)

1955
[15]

In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops

Layne, R., Hannuna, S., Camplani, M., Hall, J., Hospedales, T.M., Xiang, T., Mirmehdi, M., Damen, D.: A dataset for persistent multi-target multi-camera tracking in rgb-d. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 47–55 (2017)

2017
[16]

Artificial Intelligence Surgery4(2), 92–108 (2024)

Liu, B., Soenens, G., Villarreal, J., Jopling, J., Van Herzeele, I., Rau, A., Yeung- Levy, S.: A human mesh-centered approach to action recognition in the operating room. Artificial Intelligence Surgery4(2), 92–108 (2024)

2024
[17]

International journal of computer vision129, 548–578 (2021) Title Suppressed Due to Excessive Length 11

Luiten, J., Osep, A., Dendorfer, P., Torr, P., Geiger, A., Leal-Taixé, L., Leibe, B.: Hota: A higher order metric for evaluating multi-object tracking. International journal of computer vision129, 548–578 (2021) Title Suppressed Due to Excessive Length 11

2021
[18]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops

Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 0–0 (2019)

2019
[19]

In: 2023 IEEE International conference on image processing (ICIP)

Maggiolino, G., Ahmad, A., Cao, J., Kitani, K.: Deep oc-sort: Multi-pedestrian tracking by adaptive re-identification. In: 2023 IEEE International conference on image processing (ICIP). pp. 3025–3029. IEEE (2023)

2023
[20]

Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., Fallert, J., Feussner, H., Giannarou, S., Mascagni, P., et al.: Surgical data science– fromconceptstowardclinicaltranslation.Medicalimageanalysis 76,102306(2022)

2022
[21]

In: MICCAI

Özsoy, E., Örnek, E.P., Eck, U., Czempiel, T., Tombari, F., Navab, N.: 4d-or: Semantic scene graphs for or domain modeling. In: MICCAI. Springer (2022)

2022
[22]

In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Gener- alized intersection over union. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)

2019
[23]

In: European conference on computer vision

Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: European conference on computer vision. pp. 17–35. Springer (2016)

2016
[24]

In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR)

Srivastav, V., Chen, K., Padoy, N.: Selfpose3d: Self-supervised multi-person multi- view 3d pose estimation. In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR). pp. 2502–2512 (June 2024)

2024
[25]

Srivastav, V., Issenhuth, T., Abdolrahim, K., de Mathelin, M., Gangi, A., Padoy, N.: Mvor: A multi-view rgb-d operating room dataset for 2d and 3d human pose estimation (2018)

2018
[26]

arXiv preprint arXiv:2408.13003 (2024)

Stanojević, V., Todorović, B.: Boosttrack++: using tracklet information to detect more objects in multiple object tracking. arXiv preprint arXiv:2408.13003 (2024)

2024
[27]

In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16

Tu, H., Wang, C., Zeng, W.: Voxelpose: Towards multi-camera 3d human pose estimation in wild environment. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16. pp. 197–

2020
[28]

Advances in Neural Information Processing Systems 37, 107984–108011 (2024)

Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., et al.: Yolov10: Real-time end-to-end object detection. Advances in Neural Information Processing Systems 37, 107984–108011 (2024)

2024
[29]

Medical Image Analysis p

Wang, T.D., Bastian, L., Czempiel, T., Heiliger, C., Navab, N.: Beyond role-based surgical domain modeling: Generalizable re-identification in the operating room. Medical Image Analysis p. 103687 (2025)

2025
[30]

In: European conference on computer vision

Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., Wang, X.: Bytetrack: Multi-object tracking by associating every detection box. In: European conference on computer vision. pp. 1–21. Springer (2022)

2022
[31]

In: CVPR (2025)

Özsoy, E., Pellegrini, C., Czempiel, T., Tristram, F., Yuan, K., Bani-Harouni, D., Eck, U., Busam, B., Keicher, M., Navab, N.: Mm-or: A large multimodal operating room dataset for semantic understanding of high intensity surgical environments. In: CVPR (2025)

2025

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

arXiv preprint arXiv:2206.14651 (2022)

Aharon, N., Orfaig, R., Bobrovsky, B.Z.: Bot-sort: Robust associations multi- pedestrian tracking. arXiv preprint arXiv:2206.14651 (2022)

arXiv 2022

[2] [2]

MICCAI 2022 AE-CAI Workshop11(4), 1113–1121 (2023)

Bastian, L., Czempiel, T., Heiliger, C., Karcz, K., Eck, U., Busam, B., Navab, N.: Know your sensors—a modality study for surgical action classification. MICCAI 2022 AE-CAI Workshop11(4), 1113–1121 (2023)

2022

[3] [3]

In: MICCAI

Bastian, L., Derkacz-Bogner, D., Wang, T.D., Busam, B., Navab, N.: Segmentor: Obtaining efficient operating room semantics through temporal propagation. In: MICCAI. pp. 57–67. Springer (2023)

2023

[4] [4]

IJCARS pp

Bastian, L., Wang, T.D., Czempiel, T., Busam, B., Navab, N.: Disguisor: holistic face anonymization for the operating room. IJCARS pp. 1–7 (2023)

2023

[5] [5]

Machine Vision and Applications27, 1035–1046 (2016)

Belagiannis, V., Wang, X., Shitrit, H.B.B., Hashimoto, K., Stauder, R., Aoki, Y., Kranzfelder, M., Schneider, A., Fua, P., Ilic, S., et al.: Parsing human skeletons in an operating room. Machine Vision and Applications27, 1035–1046 (2016)

work page 2016

[6] [6]

IEEE Transactions on Pattern Analysis and Machine Intelligence 33(9), 1806–1819 (2011)

Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k- shortest paths optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(9), 1806–1819 (2011)

work page 2011

[7] [7]

EURASIP Journal on Image and Video Processing2008, 1–10 (2008)

Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP Journal on Image and Video Processing2008, 1–10 (2008)

2008

[8] [8]

In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Cao, J., Pang, J., Weng, X., Khirodkar, R., Kitani, K.: Observation-centric sort: Rethinking sort for robust multi-object tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9686–9696 (2023)

2023