arxiv: 2511.00510 · v2 · submitted 2025-11-01 · 💻 cs.CV · cs.RO· eess.IV

OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback

Kai Luo , Hao Shi , Kunyu Peng , Fei Teng , Sheng Wu , Kaiwei Wang , Kailun Yang This is my paper

Pith reviewed 2026-05-18 01:51 UTC · model grok-4.3

classification 💻 cs.CV cs.ROeess.IV

keywords omnidirectional multi-object trackingpanoramic distortiontrajectory feedback360 degree field of viewmulti-object trackingfeature stabilizationlong-term associationrobotic perception

0 comments p. Extension

The pith

OmniTrack++ refines panoramic multi-object tracking by feeding trajectory cues back to stabilize features and associations under 360-degree distortion.

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

The paper establishes that a feedback-driven framework can overcome panoramic distortion, large search spaces, and identity ambiguity in omnidirectional multi-object tracking by progressively incorporating trajectory information from prior detections. DynamicSSM blocks first normalize features to reduce geometric warping, FlexiTrack Instances then apply trajectory feedback for precise short-term localization, and ExpertTrack Memory uses a mixture-of-experts structure to retain appearance cues across fragmented sequences. Tracklet Management further adapts between full end-to-end and detection-based modes based on scene conditions. A sympathetic reader would care because these mechanisms enable more reliable tracking in wide-field robotic and surveillance settings where conventional narrow-view trackers lose objects or switch identities.

Core claim

OmniTrack++ adopts a feedback-driven framework that progressively refines perception with trajectory cues to address panoramic distortion, large search space, and identity ambiguity under a 360 degree FoV. A DynamicSSM block first stabilizes panoramic features, implicitly alleviating geometric distortion. On top of normalized representations, FlexiTrack Instances use trajectory-informed feedback for flexible localization and reliable short-term association. To ensure long-term robustness, an ExpertTrack Memory consolidates appearance cues via a Mixture-of-Experts design, enabling recovery from fragmented tracks and reducing identity drift. Finally, a Tracklet Management module adaptively sw

What carries the argument

The feedback-driven framework that progressively refines perception by injecting trajectory cues into feature stabilization, localization, and long-term memory consolidation.

If this is right

Stabilized panoramic features reduce the impact of geometric distortion on subsequent detection and association steps.
Trajectory-informed feedback in FlexiTrack Instances improves short-term localization accuracy in large search regions.
ExpertTrack Memory with mixture-of-experts design recovers from track fragmentation and lowers long-term identity drift.
Adaptive switching in Tracklet Management balances accuracy and efficiency across varying scene dynamics.
The EmboTrack benchmark with QuadTrack and BipTrack sequences provides a testbed that spans diverse robotic motion patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar trajectory feedback loops could be tested on fisheye or other non-panoramic wide-field cameras used in vehicles or drones.
The mixture-of-experts memory structure might be adapted to handle frequent occlusions in crowded indoor scenes.
Combining the adaptive mode switch with depth or motion sensors could further reduce errors during rapid robot turns.
Long-term memory consolidation may offer gains in multi-camera setups where tracks cross between overlapping 360-degree views.

Load-bearing premise

Trajectory cues from earlier tracks can be used to refine current features and associations without accumulating errors or causing identity switches when panoramic distortion and large search spaces are present.

What would settle it

Ablating the DynamicSSM block or the ExpertTrack Memory on JRDB or EmboTrack and measuring whether HOTA scores fall or identity switches rise relative to the full OmniTrack++ model.

Figures

Figures reproduced from arXiv: 2511.00510 by Fei Teng, Hao Shi, Kailun Yang, Kai Luo, Kaiwei Wang, Kunyu Peng, Sheng Wu.

**Figure 2.** Figure 2: Overview of the EmboTrack benchmark (BipTrack and QuadTrack) and MOT results on the QuadTrack test set. (a) BipTrack [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Pipeline overview of OmniTrack++. At frame t, the panoramic input is processed by a shared backbone, a DynamicSSM block, and an encoder to produce learnable instances for the current frame. In parallel, FlexiTrack Instances from frame t−1 are retrieved from the ExpertTrack Memory. These two sets of tokens are concatenated and fed into the decoder to generate object proposals. A Dual-Branch Adapter then ro… view at source ↗

**Figure 4.** Figure 4: The proposed DynamicSSM Block is integrated into a standard DAB encoder as a plug-in enhancement. Rather than explicitly modeling panoramic geometry, it implicitly calibrates spatial and photometric feature distributions to mitigate geometric distortions and illumination variation. This adaptation yields more robust and stable representations, enabling more reliable decoding and multi-object tracking in pa… view at source ↗

**Figure 5.** Figure 5: ExpertTrack Memory framework. The module integrates long-term Stable Identity Memory (SIM) and short-term Dynamic Interaction Memory (DIM) to jointly maintain identity consistency and adapt to rapid appearance changes under panoramic distortions. A Hierarchical Memory Controller (HMC) assigns high-confidence features to SIM and recent-frame updates to DIM. A Router then selects the top-Kr features across b… view at source ↗

**Figure 7.** Figure 7: Instance motion trajectories over time. The horizontal axis [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: Distribution of bounding box sizes in EmboTrack. The plots illustrate the box size distributions of the QuadTrack and BipTrack datasets across both training and test sets. transitions, complicating object detection and association. (ii) QuadTrack: Collected using a quadruped robotic platform ( [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

**Figure 9.** Figure 9: Visualization of query localization. The first row [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗

**Figure 10.** Figure 10: Effects of the trajectory initialization threshold and update [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

**Figure 11.** Figure 11: Comparison of different MOT methods [15], [30], [113] on the JRDB dataset [120], visualized for frames 300∼310 (every other frame) of the sequence nvidia-aud-2019-04-18 0. As shown in the visualizations, OmniTrack++ demonstrates robust tracking performance, effectively maintaining consistent associations even under challenging conditions such as occlusions and motion dynamics. 5.3.4 Analysis of the initia… view at source ↗

**Figure 12.** Figure 12: Analysis of failure cases on JRDB [120], highlighting scenarios where OmniTrack++ struggles compared to ByteTrack [30]. two frames. A similar issue can be observed in the second row with ByteTrack [30], where partial occlusion leads to trajectory fragmentation and identity inconsistency. In contrast, OCSORT [113] (third row) successfully preserves the target trajectory under occlusion, yet introduces ov… view at source ↗

read the original abstract

To address panoramic distortion, large search space, and identity ambiguity under a 360{\deg} FoV, OmniTrack++ adopts a feedback-driven framework that progressively refines perception with trajectory cues. A DynamicSSM block first stabilizes panoramic features, implicitly alleviating geometric distortion. On top of normalized representations, FlexiTrack Instances use trajectory-informed feedback for flexible localization and reliable short-term association. To ensure long-term robustness, an ExpertTrack Memory consolidates appearance cues via a Mixture-of-Experts design, enabling recovery from fragmented tracks and reducing identity drift. Finally, a Tracklet Management module adaptively switches between end-to-end and tracking-by-detection modes according to scene dynamics, offering a balanced and scalable solution for panoramic MOT. To support rigorous evaluation, we establish the EmboTrack benchmark, a comprehensive dataset for panoramic MOT that includes QuadTrack, captured with a quadruped robot, and BipTrack, collected with a bipedal wheel-legged robot. Together, these datasets span wide-angle environments and diverse motion patterns, providing a challenging testbed for real-world panoramic perception. Extensive experiments on JRDB and EmboTrack demonstrate that OmniTrack++ achieves state-of-the-art performance, yielding substantial HOTA improvements of +3.94 on JRDB and +15.03 on QuadTrack over the original OmniTrack. These results highlight the effectiveness of trajectory-informed feedback, adaptive paradigm switching, and robust long-term memory in advancing panoramic multi-object tracking. Datasets and code will be made available at https://github.com/xifen523/OmniTrack.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

OmniTrack++ adds a feedback-driven extension with new components and a robotic panoramic benchmark, showing clear HOTA gains over the prior version, though the evaluation lacks enough ablations to fully confirm robustness.

read the letter

The main takeaway is that OmniTrack++ improves on the original by introducing a feedback-driven system for omnidirectional multi-object tracking, with measurable gains on existing and new benchmarks. What is new here is the combination of DynamicSSM for panoramic feature stabilization, FlexiTrack Instances that leverage trajectory feedback for flexible localization and short-term association, ExpertTrack Memory that uses a Mixture-of-Experts to consolidate cues and reduce identity drift over time, and the Tracklet Management module that switches tracking modes adaptively. They also create the EmboTrack benchmark with QuadTrack and BipTrack datasets collected from different robot types to test diverse wide-angle motion patterns. The paper does well in targeting real issues like distortion and large search spaces in 360 FoV setups, and the reported HOTA improvements of +3.94 on JRDB and +15.03 on QuadTrack suggest the approach has practical value for robotics applications. On the soft spots, the evaluation could be stronger. While the abstract highlights the SOTA results, it does not detail the baselines used, any ablation studies, or statistical measures like error bars. The potential for the feedback loop in ExpertTrack Memory to accumulate errors or identity switches under fragmented tracks and panoramic distortion is a legitimate concern without specific analysis of drift or identity switch rates versus track duration. If those checks are missing from the full paper, the larger gain on QuadTrack might not hold up as robustly as claimed. This work is aimed at researchers focused on multi-object tracking in panoramic or wide-field-of-view settings, particularly for robotic perception. A reader interested in engineering solutions for long-term association in challenging environments would get value from the new benchmark and the described components. It deserves a serious referee because it introduces new data and a coherent extension of prior methods with promised code release. I recommend sending this to peer review, with attention to adding more detailed ablations on the memory and feedback parts.

Referee Report

2 major / 2 minor

Summary. The manuscript presents OmniTrack++, a feedback-driven framework for omnidirectional multi-object tracking that addresses panoramic distortion, large search spaces, and identity ambiguity under 360° FoV. Key components include a DynamicSSM block for stabilizing panoramic features, FlexiTrack Instances that use trajectory-informed feedback for localization and short-term association, an ExpertTrack Memory module employing a Mixture-of-Experts design to consolidate appearance cues and recover from fragmented tracks, and a Tracklet Management module for adaptive switching between end-to-end and tracking-by-detection modes. The authors introduce the EmboTrack benchmark (including QuadTrack and BipTrack datasets) and report state-of-the-art results with HOTA improvements of +3.94 on JRDB and +15.03 on QuadTrack over the original OmniTrack.

Significance. If the empirical claims hold under rigorous verification, this work advances panoramic MOT by demonstrating how trajectory feedback can refine perception in challenging wide-FoV settings. The EmboTrack benchmark is a useful addition for evaluating methods on robotic platforms with diverse motion patterns. The Mixture-of-Experts approach in ExpertTrack Memory offers a plausible mechanism for long-term robustness. However, the overall significance is limited by insufficient experimental detail, which prevents full assessment of whether the reported gains generalize or stem from the proposed feedback mechanisms.

major comments (2)

[Section 4] Section 4 (Experiments): The SOTA claims rest on HOTA deltas of +3.94 on JRDB and +15.03 on QuadTrack, yet the section provides no details on the exact baselines, per-component ablation studies (e.g., isolating DynamicSSM or ExpertTrack Memory), error bars, dataset splits, or statistical significance tests. This makes it impossible to verify that the gains arise from the trajectory-informed feedback rather than implementation choices or post-hoc tuning.
[Section 3.3] Section 3.3 (ExpertTrack Memory): The description of the Mixture-of-Experts design for recovering from fragmented tracks and reducing identity drift lacks any analysis of error propagation in the feedback loop, per-component identity-switch rates versus track duration, or quantification of how panoramic distortion impacts the normalized representations used by FlexiTrack. Without such analysis, the central premise that the memory module reliably avoids cumulative errors under large-FoV fragmentation remains untested.

minor comments (2)

[Abstract] The abstract states that datasets and code will be released at a GitHub link, but the manuscript should explicitly confirm in the experiments section that the link is active and includes the EmboTrack data splits used for the reported results.
[Section 3] Notation for components (DynamicSSM, FlexiTrack, ExpertTrack Memory) would benefit from a single overview figure or pseudocode algorithm in Section 3 to clarify data flow between the feedback modules.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight areas where additional detail can strengthen the presentation of our results. We address each major comment below and commit to incorporating the suggested analyses and clarifications in the revised manuscript.

read point-by-point responses

Referee: [Section 4] Section 4 (Experiments): The SOTA claims rest on HOTA deltas of +3.94 on JRDB and +15.03 on QuadTrack, yet the section provides no details on the exact baselines, per-component ablation studies (e.g., isolating DynamicSSM or ExpertTrack Memory), error bars, dataset splits, or statistical significance tests. This makes it impossible to verify that the gains arise from the trajectory-informed feedback rather than implementation choices or post-hoc tuning.

Authors: We agree that the current experimental section lacks the granularity needed for full verification. In the revised manuscript we will expand Section 4 to provide: explicit implementation details and hyper-parameters for all baselines; comprehensive per-component ablations that isolate DynamicSSM, FlexiTrack Instances, ExpertTrack Memory, and Tracklet Management; error bars obtained from multiple independent runs; precise descriptions of the training/validation/test splits on JRDB and EmboTrack; and statistical significance tests (e.g., paired t-tests) on the reported HOTA improvements. These additions will allow readers to confirm that the observed gains originate from the trajectory-feedback mechanisms rather than other factors. revision: yes
Referee: [Section 3.3] Section 3.3 (ExpertTrack Memory): The description of the Mixture-of-Experts design for recovering from fragmented tracks and reducing identity drift lacks any analysis of error propagation in the feedback loop, per-component identity-switch rates versus track duration, or quantification of how panoramic distortion impacts the normalized representations used by FlexiTrack. Without such analysis, the central premise that the memory module reliably avoids cumulative errors under large-FoV fragmentation remains untested.

Authors: We concur that further empirical analysis is required to substantiate the robustness claims for ExpertTrack Memory. In the revision we will augment Section 3.3 and the experimental results with: a study of error propagation through the feedback loop; identity-switch rates broken down by track duration and by component; and quantitative measurements (together with qualitative examples) of how panoramic distortion affects the normalized representations inside FlexiTrack, demonstrating the stabilizing effect of the Mixture-of-Experts design. These analyses will directly test the premise that the memory module limits cumulative errors under large-FoV fragmentation. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on external benchmarks and prior method comparisons

full rationale

The manuscript describes an algorithmic pipeline (DynamicSSM for feature stabilization, FlexiTrack for trajectory-informed localization, ExpertTrack Memory with Mixture-of-Experts, and adaptive Tracklet Management) evaluated on JRDB and the newly introduced EmboTrack (QuadTrack/BipTrack) datasets. Reported HOTA gains (+3.94 on JRDB, +15.03 on QuadTrack) are presented as direct experimental outcomes against the original OmniTrack baseline. No equations, fitted parameters, or predictions are defined in terms of the target metrics; no self-citation chain is invoked to justify uniqueness or force a result; the derivation chain consists of design choices validated externally rather than reducing to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach depends on standard deep-learning assumptions for feature extraction and association plus the domain-specific premise that trajectory cues are informative for panoramic correction; no explicit free parameters or new physical entities are detailed in the abstract.

axioms (1)

domain assumption Trajectory cues from prior detections provide reliable feedback that can progressively refine perception, localization, and association under 360-degree distortion.
This premise underpins the entire feedback-driven framework and the roles of DynamicSSM, FlexiTrack, and ExpertTrack Memory.

pith-pipeline@v0.9.0 · 5831 in / 1613 out tokens · 44862 ms · 2026-05-18T01:51:39.776584+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

A DynamicSSM block first stabilizes panoramic features... ExpertTrack Memory consolidates appearance cues via a Mixture-of-Experts design... Tracklet Management module adaptively switches between end-to-end and tracking-by-detection modes
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Extensive experiments on JRDB and EmboTrack... HOTA improvements of +3.94 on JRDB and +15.03 on QuadTrack

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
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