Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing

Alexandros Gkillas; Aris S. Lalos; Maria Damanaki; Nikos Piperigkos

REVIEW 3 major objections 4 minor 19 references

Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that a single graph-Laplacian least-squares solve over multi-agent bounding boxes refines their 3D centroids, and that feeding these refined detections through two association stages improves cooperative multi-object…

desk verdict The central GLP smoothing step is an identity under the authors' own definitions, and the paper never fixes the ordering that could make it nontrivial; the two-stage association is fine but can't carry the paper as written. read the letter →

arxiv 2506.09469 v1 pith:ZYPUIUIH submitted 2025-06-11 cs.CV

Maria Damanaki , Nikos Piperigkos , Alexandros Gkillas , Aris S. Lalos This is my paper

classification cs.CV

keywords cooperativemulti-objecttrackinggraphsignalprocessingLaplacianvehicle-to-vehicleperception3DLiDARdetectionboundingboxfusionKalmanfilteringdataassociation

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

The paper proposes a cooperative multi-object tracking framework in which the 3D bounding boxes detected by two (or more) vehicles are viewed as nodes of a fully connected graph, and a Graph Laplacian Processing step is used to refine the centroid of each box. The stated goal is to smooth positional error across overlapping detections before they are associated with existing tracks, so that a Kalman filter update receives less noisy measurements. The authors claim this graph-based fusion, combined with a two-stage association that re-associates unmatched tracks, improves both localization precision and tracking accuracy on the real-world V2V4Real dataset, outperforming the DMSTrack and V2V4Real baselines on most metrics.

What carries the argument

The key machinery is the extended Graph Laplacian system of Eq. (8) and Eq. (10). A fully connected graph is built by treating every detection from every cooperating agent as a node; the Laplacian $L^t$ encodes the connectivity, the differential vector $\delta^t$ records each node's offset from the barycenter of its neighbors, and the anchor vector $a^t$ stores the original detection coordinates. The least-squares solution of the extended system is the refined centroid estimate in each of the $x$, $y$, $z$ axes, and this estimate is what enters the association and Kalman-update stages.

What would settle it

Compute $v^{t*}$ from Eq. (10) on any V2V4Real test frame using the paper's $\delta^t$ and $a^t$, and compare it with the raw anchor vector $a^t$; if they are identical for every axis, the graph-Laplacian smoothing step performs no refinement. A complementary experiment would inject synthetic noise of known variance into detection centroids and check whether the output error variance is actually smaller than the input.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the spatial coherence of multi-agent detections can be exploited by a single least-squares optimization: the extended Laplacian matrix $\tilde{L}^t = [L^t; I]^T$ is applied to the unknown refined centroids $v^t$, and the measurement vector $b^t = [\delta^t, a^t]^T$ blends differential coordinates (offsets to neighboring detections) with anchor points (the raw detection coordinates). The solution $v^{t*} = ((\tilde{L}^t)^T \tilde{L}^t)^{-1}(\tilde{L}^t)^T b^t$ is taken to be a refined, fused centroid in each spatial axis, and these refined boxes are then matched to tracks in one or two association stages. The authors report that this pipeline reduces positional noise and increases the number of objects tracked for most of their lifetime.

Load-bearing premise

The method assumes that the differential coordinates and the anchor points in Eq. (10) carry independent information, so that blending them moves the centroids to a less noisy position; in the paper's construction both are computed from the same detection coordinates, which would make the blend return the detection coordinates unchanged.

Editorial extensions

If this is right

If the smoothing claim holds, the positional error of fused detections is reduced before the Kalman update, which would explain the reported AMOTP improvements.
Because all detections are processed as one graph rather than sequentially per agent, the framework extends to additional connected vehicles by adding nodes and edges to the same optimization.
Two-stage association lets tracks left unmatched after the first stage be re-associated with a second refined set of detections, which the paper ties to higher Mostly Tracked scores and fewer false track terminations.
The communication payload is only the seven parameters of each bounding box, so the cooperative refinement is feasible in real time under the reported settings.

Reading between the lines

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

An editor's inference from the equations as written: because $\delta^t$ is defined from the same detection coordinates that later populate $a^t$, the identity $\delta^t = L^t a^t$ makes $v^{t*} = a^t$ in Eq. (10). Taken literally, the graph step would return the raw centroids unchanged, and any measured gains would come from the two-stage association and Kalman filtering rather than from Laplacian
A testable consequence: on any frame where the paper's construction is used verbatim, the refined coordinates and the raw anchor coordinates should be numerically identical; a reader can verify this directly on V2V4Real detections.
A non-vacuous variant would define anchors from an independent source, such as the previous time step's fused estimate or a central estimate, so that the least-squares blend genuinely trades off smoothness against fidelity.

Share X Bluesky LinkedIn Reddit HN

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

Quick take: the paper's headline mechanism—Graph Laplacian Processing for fusing and smoothing multi-agent detections—collapses into an identity under one natural reading of the equations, and the paper never specifies the node ordering that would determine whether the least-squares step does anything at all. That is a load-bearing ambiguity, not a cosmetic one.

What's genuinely worth noting: applying graph Laplacian localization to cooperative 3D MOT is a sensible idea, and the two-stage association (TSA) for rescuing unmatched tracks is a reasonable engineering extension. The experiments on V2V4Real are a legitimate choice, and the ablation against DMSTrack on a few sequences gives a useful comparison. If the GLP step were clearly specified and shown to reduce noise, this could be a useful subfield contribution.

The problems: Eq. (10) solves min ||L v - delta||^2 + ||v - a||^2. Section III-A defines delta from the current detections' centroids, so delta = L p for some ordering of the detection vector p. The anchors a^t in Eq. (11) are also built from those centroids. If the graph nodes are ordered [j-matched, i-matched, i-unmatched, j-unmatched], then a^t = p, the stacked system is consistent with v* = p, and the 'refined' output is exactly the raw input. If the ordering is different, the anchors are cross-anchored and the step is a weighted average—but that ordering is never stated, and the paper doesn't prove that its outputs reduce localization error. So the central claim, that GLP smooths bounding boxes, is unverified as written.

The empirical reporting also has issues: no error bars, no code or data, and the abstract's 'up to 19.32%' doesn't match Table I's 17.3% for AMOTA. The baselines use different detectors (PointPillar vs CoBEV), so the comparison is not entirely apples-to-apples.

None of this means the underlying idea is worthless. The two-stage association may explain many of the reported gains, and the authors' earlier work on graph Laplacian localization is a real source to build on. But this manuscript, as written, doesn't establish the claimed mechanism.

Who it's for: researchers working on cooperative tracking or graph signal processing for fusion might want to see the idea, but they shouldn't take the results at face value. I'd send it to peer review only with the expectation of major revision—asking for a precise node ordering, a proof that the optimization is not the identity, code, and error bars. If the venue wants clean papers, a desk reject is defensible, but the topic is relevant enough that a good referee could help the authors salvage it.

Referee Report

3 major / 4 minor

Summary. The paper proposes a cooperative 3D multi-object tracking framework for vehicle-to-vehicle scenarios. It builds a fully connected graph on the detected bounding boxes of two agents, applies a Graph Laplacian least-squares optimization to refine the 3D centroids, and then associates the refined detections with existing tracks in either one association stage (AOS) or two association stages (TSA). The experiments on the V2V4Real dataset compare the proposed methods with DMSTrack and V2V4Real+CoBEV. The central claim is that Graph Laplacian Processing smooths positional error and fuses multi-agent detections, leading to better localization and tracking accuracy.

Significance. Cooperative tracking on real-world LiDAR data is timely, and the two-stage association idea, in which unmatched tracks are re-associated with a second fused detection set, is a plausible way to reduce premature track termination. The dataset choice and the use of standard MOT metrics are appropriate. However, the mathematical core does not deliver the claimed smoothing: the quantities entering the GLP least-squares problem are all derived from the same raw detections, the node ordering is never fixed, and under the ordering implied by Eq. (11) the output is exactly the input. The reported gains are therefore not causally attributable to the GLP step, and no code, data, or error bars are provided to compensate. If the GLP step were replaced by an estimator with genuinely independent anchors, the association machinery might be of interest, but that is not what is demonstrated here.

major comments (3)

[Section III-A, Eq. (10)] The GLP refinement step is degenerate as written. The differential-coordinate vector delta^t is computed in Section III-A from the current detection centroids, and the anchor vector a^t in Eq. (11) is formed from those same centroids. If the graph nodes are ordered exactly as in Eq. (11), then delta^t = L^t a^t, so b^t = [delta^t; a^t] = \tilde L^t a^t, and Eq. (10) yields v* = a^t; the "refined" detection set G^t is then identical to the raw input, contradicting the abstract's claim that position error is smoothed. The manuscript never fixes the row/block ordering of V^t, and no other ordering is justified, so the claimed Laplacian smoothing is not a well-defined operation.
[Section III-C, Eqs. (12)-(13)] The TSA variant inherits the same circularity. Both anchor vectors a^t_ij and a^t_ji are concatenations of the raw matched and unmatched centroids, and the differential coordinates delta^t are computed from those same centroids. Swapping the matched entries between agents does not introduce independent anchor information. The paper provides no proof or ablation showing that G^t_ij and G^t_ji differ from the raw detections or reduce localization error; consequently, the TSA-versus-AOS gains reported in Table II cannot be attributed to the Graph Laplacian step.
[Section IV, Tables I-II] The numerical evidence does not support the claim of significance. The tables report single runs without error bars or per-sequence variance; the comparison base for the percentages in Table I is unclear because the caption refers to the "maximum deviation" of the baselines while the text treats the values as direct relative improvements; and there is no baseline that runs the same AOS/TSA association stages on raw, unrefined detections. Without such a baseline, even the observed metric differences cannot be causally linked to the proposed Graph Laplacian processing.

minor comments (4)

[Eq. (8)] The stated dimensions and the transpose in the definition of \tilde L^t are inconsistent; please clarify the block structure (L^t stacked over I^t) and the normal equations in Eq. (10).
[Abstract and Table II] The abstract reports an improvement "up to 19.32%", but Table II shows 19.82% (AMOTP, Sequence 0002) and Table I shows 17.3% (AMOTA); please reconcile these numbers.
[Section IV-A, Table I, Fig. 3] The metrics list includes MOTP, but Table I does not report it while Figure 3 uses it; state explicitly which metric is reported where.
[Editorial] Minor editorial issues include the typesetting of "CA Vs", the incomplete caption of Figure 2, and Algorithm 1 line 15, which omits the termination step for the first association stage.

Circularity Check

1 steps flagged · score 6.0 of 10

GLP refinement reduces to raw input under the anchor ordering in Eq. (11); the central smoothing claim is not well-defined.

self definitional [Section III-A and Eqs. (10)-(11)]
"δ(m,t) = PMj n=0 (xi,m − xj,n) + PMi n=0 (xi,m − xi,n) ... The anchors vector at is formed for x axis as: at = [mxj,m mxi,m uxi,m uxj,m]T"

The differential vector δt is computed from the raw detection centroids, so δt = Lt pt for the centroid vector pt ordered by graph nodes. Equation (11) populates the anchor vector with those same raw centroids. If the graph node order is taken to match the order in Eq. (11), then at = pt, and the stacked measurement is bt = [δt; at] = Ltilde pt. Substituting into Eq. (10) gives vt* = ((Ltilde^T Ltilde)^-1 Ltilde^T)(Ltilde pt) = pt. Thus the 'refined and fused' detection set Gt equals the raw input exactly; the claimed Laplacian smoothing of position error is identity by construction. The paper never specifies an alternative node ordering and provides no code, so this vacuous branch is not excluded.

full rationale

The paper's contribution is not self-citation: GLP is a standard tool [16] and the V2V4Real benchmark comparison is external evidence. The circularity is instead in the internal derivation. Section III-A builds δ from the same noisy centroids that Section III-B puts into the anchor vector, and Eq. (10) is a least-squares fit to exactly those quantities. Under the consistent node order induced by Eq. (11), b = Ltilde p and v* = p, so the 'refined' bounding boxes are the input detections by construction. Because the manuscript leaves the node/block ordering unspecified and supplies no code, the central claim that GLP smooths or fuses detections is unverified; a non-identity outcome would require a deliberately mismatched anchor order that the text does not define. Score 6: partial circularity in the central refinement step, while the tracking-association pipeline and benchmark numbers remain independent content.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The ledger shows that the paper's only hand-set parameters are tracking thresholds and covariance matrices, and that the decisive move is the definition of delta from the same values as anchors. That definition is what makes the optimization vacuous.

free parameters (4)

track confirmation threshold (hits) = 3
Set in Section IV-A to balance robustness and adaptability, without sensitivity analysis.
track termination threshold (age) = 2
Set in Section IV-A; no sensitivity analysis.
Kalman noise covariances Q and R = not reported
Required by Eq. (4) and (5) but not specified; replication requires guessing.
implicit anchor weight = 1
The least-squares cost weights differential and anchor terms equally; this choice makes the solution equal to the anchors.

assumptions (4)

domain assumption All agent detections are projected into a common global coordinate frame without residual registration error.
Section II-A describes projecting D_t_i to a global coordinate system but does not model calibration error.
ad hoc to paper The differential coordinates are computed from the same detections as the anchors, i.e., delta = L a.
Section III-A defines delta from current detections and Eq. (11) puts the same values into a, which forces the identity solution.
domain assumption A constant-velocity linear Kalman filter describes object motion.
Eq. (1) and (2) use a constant velocity transition model; validity for V2V4Real is not examined.
standard math 3D IoU and Hungarian assignment form a sufficient association metric.
Standard MOT practice, not new to this paper.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing." pith.science (2026). https://pith.science/paper/ZYPUIUIH

@misc{pith2026250609469,
  author       = {Pith},
  title        = {Pith review of: Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZYPUIUIH}},
  note         = {Machine review of arXiv:2506.09469}
}

read the original abstract

Multi-Object Tracking (MOT) plays a crucial role in autonomous driving systems, as it lays the foundations for advanced perception and precise path planning modules. Nonetheless, single agent based MOT lacks in sensing surroundings due to occlusions, sensors failures, etc. Hence, the integration of multiagent information is essential for comprehensive understanding of the environment. This paper proposes a novel Cooperative MOT framework for tracking objects in 3D LiDAR scene by formulating and solving a graph topology-aware optimization problem so as to fuse information coming from multiple vehicles. By exploiting a fully connected graph topology defined by the detected bounding boxes, we employ the Graph Laplacian processing optimization technique to smooth the position error of bounding boxes and effectively combine them. In that manner, we reveal and leverage inherent coherences of diverse multi-agent detections, and associate the refined bounding boxes to tracked objects at two stages, optimizing localization and tracking accuracies. An extensive evaluation study has been conducted, using the real-world V2V4Real dataset, where the proposed method significantly outperforms the baseline frameworks, including the state-of-the-art deep-learning DMSTrack and V2V4Real, in various testing sequences.

Figures

Figures reproduced from arXiv: 2506.09469 by the authors.

**Figure 2.** TSA Graph Lap-CoMOT of the Graph Lap-CoMOT approach C. Two Stages Association (TSA) Graph Lap-CoMOT The Two Stages Association (TSA) Graph Lap-CoMOT technique (Fig.2) is an advanced extension of the AOS Graph Lap-CoMOT. This method designs two stage association modules to capture unmatched trajectories from the first stage and combine obtained detections from different agents in slightly modified definition of ancho… view at source ↗

**Figure 3.** Impact of increasing graph topology size on MOTP on average. The [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Qualitative results of CoMOT based on assessing tracking accuracy [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

19 extracted references · 15 canonical work pages

[1]

Bevsort: Bird eye view lidar multi object tracking,

L. Alfeqy, et al. , “Bevsort: Bird eye view lidar multi object tracking,” in 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELE- CON), 2024, pp. 7–12

work page 2024
[2]

V oxelnext: Fully sparse voxelnet for 3d object detection and tracking,

Y . Chen, et al., “V oxelnext: Fully sparse voxelnet for 3d object detection and tracking,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 21 674–21 683

work page 2023
[3]

3d multi-object tracking in point clouds based on prediction confidence-guided data association,

H. Wu, et al. , “3d multi-object tracking in point clouds based on prediction confidence-guided data association,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5668–5677, 2022

work page 2022
[4]

Center-based 3d object detection and tracking,

T. Yin, et al. , “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 11 784–11 793

work page 2021
[5]

3d multi-object tracking: A baseline and new evaluation metrics,

X. Weng, et al., “3d multi-object tracking: A baseline and new evaluation metrics,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 10 359–10 366

2020
[6]

Graph laplacian diffusion localization of con- nected and automated vehicles,

N. Piperigkos, et al. , “Graph laplacian diffusion localization of con- nected and automated vehicles,” IEEE Transactions on Intelligent Trans- portation Systems , vol. 23, no. 8, pp. 12 176–12 190, 2022

work page 2022
[7]

Probabilistic 3d multi-object cooperative tracking for autonomous driving via differentiable multi-sensor kalman filter,

H.-K. Chiu, et al. , “Probabilistic 3d multi-object cooperative tracking for autonomous driving via differentiable multi-sensor kalman filter,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 18 458–18 464

2024
[8]

Multi-objective multi-agent planning for discovering and tracking multiple mobile objects,

H. Nguyen, et al., “Multi-objective multi-agent planning for discovering and tracking multiple mobile objects,” IEEE Transactions on Signal Processing, vol. 72, pp. 3669–3685, 2024

work page 2024

Show all 19 references

[9]

V2v4real: A real-world large-scale dataset for vehicle- to-vehicle cooperative perception,

R. Xu, et al. , “V2v4real: A real-world large-scale dataset for vehicle- to-vehicle cooperative perception,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 13 712– 13 722

2023
[10]

Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication,

R. Xu, et al., “Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication,” in 2022 Interna- tional Conference on Robotics and Automation (ICRA) , 2022, pp. 2583– 2589

2022
[11]

Multi-agent deep reinforcement learning for multi- object tracker,

M. Jiang, et al. , “Multi-agent deep reinforcement learning for multi- object tracker,” IEEE Access , vol. 7, pp. 32 400–32 407, 2019

2019
[12]

Collaborative multi-object tracking with conformal un- certainty propagation,

S. Su, et al. , “Collaborative multi-object tracking with conformal un- certainty propagation,” IEEE Robotics and Automation Letters , vol. 9, no. 4, pp. 3323–3330, 2024

2024
[13]

Cooperative 3d multi-object tracking for connected and automated vehicles with complementary data association,

H. Su, et al. , “Cooperative 3d multi-object tracking for connected and automated vehicles with complementary data association,” in 2024 IEEE Intelligent V ehicles Symposium (IV) , 2024, pp. 285–291

2024
[14]

Hydro-3d: Hybrid object detection and tracking for cooperative perception using 3d lidar,

Z. Meng, et al. and Xia, “Hydro-3d: Hybrid object detection and tracking for cooperative perception using 3d lidar,” IEEE Transactions on Intelligent V ehicles, vol. 8, no. 8, pp. 4069–4080, 2023

2023
[15]

Exploring simple 3d multi-object tracking for au- tonomous driving,

C. Luo, et al. , “Exploring simple 3d multi-object tracking for au- tonomous driving,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 10 468–10 477

2021
[16]

Laplacian mesh processing,

O. Sorkine, “Laplacian mesh processing,” Eurographics (State of the Art Reports), vol. 4, no. 4, p. 1, 2005

2005
[17]

Graph laplacian processing based multi-modal localization backend for robots and autonomous systems,

N. Piperigkos, et al. , “Graph laplacian processing based multi-modal localization backend for robots and autonomous systems,” IEEE Trans- actions on Cognitive and Developmental Systems , 2024

2024
[18]

Pointpillars: Fast encoders for object detection from point clouds,

A. Lang, et al. , “Pointpillars: Fast encoders for object detection from point clouds,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 12 689–12 697

2019
[19]

Cobevt: Cooperative bird’s eye view semantic segmenta- tion with sparse transformers,

R. Xu, et al., “Cobevt: Cooperative bird’s eye view semantic segmenta- tion with sparse transformers,” arXiv preprint arXiv:2207.02202 , 2022

2022 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Bevsort: Bird eye view lidar multi object tracking,

L. Alfeqy, et al. , “Bevsort: Bird eye view lidar multi object tracking,” in 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELE- CON), 2024, pp. 7–12

work page 2024

[2] [2]

V oxelnext: Fully sparse voxelnet for 3d object detection and tracking,

Y . Chen, et al., “V oxelnext: Fully sparse voxelnet for 3d object detection and tracking,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 21 674–21 683

work page 2023

[3] [3]

3d multi-object tracking in point clouds based on prediction confidence-guided data association,

H. Wu, et al. , “3d multi-object tracking in point clouds based on prediction confidence-guided data association,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5668–5677, 2022

work page 2022

[4] [4]

Center-based 3d object detection and tracking,

T. Yin, et al. , “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 11 784–11 793

work page 2021

[5] [5]

3d multi-object tracking: A baseline and new evaluation metrics,

X. Weng, et al., “3d multi-object tracking: A baseline and new evaluation metrics,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 10 359–10 366

2020

[6] [6]

Graph laplacian diffusion localization of con- nected and automated vehicles,

N. Piperigkos, et al. , “Graph laplacian diffusion localization of con- nected and automated vehicles,” IEEE Transactions on Intelligent Trans- portation Systems , vol. 23, no. 8, pp. 12 176–12 190, 2022

work page 2022

[7] [7]

Probabilistic 3d multi-object cooperative tracking for autonomous driving via differentiable multi-sensor kalman filter,

H.-K. Chiu, et al. , “Probabilistic 3d multi-object cooperative tracking for autonomous driving via differentiable multi-sensor kalman filter,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 18 458–18 464

2024

[8] [8]

Multi-objective multi-agent planning for discovering and tracking multiple mobile objects,

H. Nguyen, et al., “Multi-objective multi-agent planning for discovering and tracking multiple mobile objects,” IEEE Transactions on Signal Processing, vol. 72, pp. 3669–3685, 2024

work page 2024