GTA: Global Tracklet Association for Multi-Object Tracking in Sports

Cheng-Yen Yang; Hsiang-Wei Huang; Jenq-Neng Hwang; Jiacheng Sun; Zhongyu Jiang

REVIEW 3 major objections 6 minor 32 references

GTA: Global Tracklet Association for Multi-Object Tracking in Sports

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A plug-and-play post-processing step that splits mixed tracklets and rejoins fragmented ones raises sports multi-object tracking accuracy on two benchmarks.

desk verdict A clean, empirical tracklet post-processor that likely delivers real gains; the ReID training split must be clarified before the SOTA claim is trusted. read the letter →

arxiv 2411.08216 v1 pith:FH23SMFC submitted 2024-11-12 cs.CV

Jiacheng Sun , Hsiang-Wei Huang , Cheng-Yen Yang , Zhongyu Jiang , Jenq-Neng Hwang This is my paper · ORCID

classification cs.CV

keywords multi-objecttrackingtrackletassociationrefinementre-identificationsportsanalyticsDBSCANclusteringhierarchicalHOTA

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 argues that the two dominant failure modes in sports player tracking are fixable after the fact by a global post-processing stage. Mix-up errors put several identities into one tracklet, while cut-off errors fragment one player across many tracklets. The proposed Global Tracklet Association (GTA) splits mixed tracklets by clustering per-box appearance embeddings with DBSCAN, then merges fragments that belong to the same player using hierarchical clustering under spatial and temporal constraints. Applied to three existing trackers, GTA raises HOTA on SportsMOT to 81.04% and on SoccerNet to 83.11%, improving SORT, ByteTrack, and Deep-EIoU across association metrics. If correct, it means tracker accuracy can be improved without retraining the tracker itself, only by cleaning its output.

What carries the argument

The machinery is the Tracklet Splitter and Tracklet Connector pair. The splitter applies DBSCAN to per-box OSNet appearance embeddings, using minimum samples, a cosine-distance neighbor threshold, and a maximum-cluster parameter, and reassigns outliers to the nearest cluster instead of discarding them. The connector builds a pairwise cosine-distance matrix that forbids merging temporally overlapping tracklets, applies spatial thresholds derived from the maximum horizontal and vertical box-center distances scaled by a factor, and merges via hierarchical clustering up to a distance threshold. All split and merge decisions reduce to comparing OSNet appearance embeddings, so the ReID model is the shared foundation of both stages.

What would settle it

Train the OSNet ReID model only on the official SportsMOT training split, apply GTA to the test split, and compare with the paper's reported HOTA values; if the gains vanish, the results relied on test-set exposure. A second check is to run GTA on a fixed-camera video where two teammates wear near-identical jerseys, and count whether the splitter creates false splits and the connector merges distinct players.

Watch

Extended reading notes

Core claim

GTA is a two-stage tracklet refinement pipeline that runs after a tracker has produced its raw output. In the splitter stage, each tracklet's box-grained appearance features, extracted by an OSNet ReID model, are clustered with a modified DBSCAN that assigns outliers to the nearest cluster and enforces a maximum number of clusters to avoid over-fragmentation. In the connector stage, a symmetric cosine-distance matrix over all tracklet pairs is built, with distance forced to 1 for temporally overlapping tracklets and for pairs whose exit/entry points violate spatial thresholds scaled to the field size, after which hierarchical clustering merges fragments until no pair distance exceeds a threshold. On SportsMOT the full pipeline improves Deep-EIoU HOTA from 77.21% to 81.04%, and on SoccerNet from 79.41% to 83.11%, with gains concentrated in AssA and IDF1 and reduced ID switches.

Load-bearing premise

Everything depends on the ReID model's appearance embeddings reliably separating different players and recognizing the same player across long gaps; the paper states the OSNet model is trained on SportsMOT but does not specify its train/test split, so if those embeddings were exposed to test-video identities, the reported gains could be inflated.

Editorial extensions

If this is right

Any existing tracker can be improved by running GTA on its output; SORT, ByteTrack, and Deep-EIoU all gain HOTA, AssA, and IDF1 while losing ID switches.
Because GTA changes association rather than detection, it leaves DetA and MOTA nearly unchanged, so the improvements come almost entirely from fixing identity assignment.
The connector's spatial constraint encodes a fixed-camera, same-side re-entry assumption, so the method transfers directly to static sports broadcasts but would need adjustment for panning or moving cameras.
Applying GTA to Deep-EIoU yields state-of-the-art results on both benchmarks, with HOTA 81.04% on SportsMOT and 83.11% on SoccerNet.

Reading between the lines

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

If ReID embeddings are the load-bearing component, then swapping OSNet for a stronger or domain-adapted ReID model should produce further gains; this is directly testable with the released code.
The same split-then-connect recipe could be applied to other tracklet-producing domains, such as vehicle tracking or multi-camera pedestrian tracking, wherever mix-up and cut-off errors dominate.
The spatial constraint assumes players re-enter from the same side of the field; a camera that pans would break that assumption, so a relaxed or learned spatial prior would be needed for such footage.

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 is a clean, empirical tracklet post-processor for sports MOT. It splits mix-up tracklets with a DBSCAN variant and merges fragments hierarchically with spatial gates. That combination is modestly new relative to Split-and-Connect and global link work, and the gains are real: consistent HOTA/AssA/IDF1 jumps across three trackers and two datasets, with the biggest wins on the weakest tracker. The code is public and the ablations are sensible.

The strongest result is that the connector alone gives most of the gain; the splitter adds a smaller boost. The method is genuinely plug-and-play in the practical sense: it consumes tracklet boxes and a frozen ReID embedding.

The main soft spot is the ReID training description. Section 4.2 says only "OSNet model trained on SportsMOT dataset" with no mention of train/test split. Since the only learned component is exactly that embedding, if the checkpoint was trained on any evaluation video or on the full dataset, the association metrics are inflated and the plug-and-play claim for unseen footage collapses. This is concrete and checkable: the code and checkpoint are public, so an editor should ask the authors to state the split and, ideally, also report results with a ReID model trained only on the official training split or on held-out sports data.

Secondary soft spots: no hyperparameter sensitivity (epsilon, s, k, alpha, beta are hand-set; beta changes by dataset), no error bars, and no comparison against existing global-link methods like Translink, AFLink, or MambaTrack. The missing comparisons matter for the SOTA label but not for the internal consistency of the reported improvements. The missing sensitivity analysis matters because the method has five free parameters and the paper does not show how stable the gains are.

Overall, I believe the paper is honest and the improvements are very likely real, assuming the ReID checkpoint is clean. It is a solid workshop-to-journal contribution, not a breakthrough. It deserves serious peer review, and the referee should make the ReID split explicit and run the public code to verify the numbers.

Referee Report

3 major / 6 minor

Summary. The paper proposes GTA (Global Tracklet Association), a two-stage post-processing method for multi-object tracking in sports. A Tracklet Splitter uses DBSCAN clustering on OSNet ReID embeddings to split tracklets that contain multiple identities, and a Tracklet Connector uses hierarchical clustering with cosine-distance and spatial constraints to merge fragmented tracklets of the same identity. The method is evaluated by applying it to SORT, ByteTrack, and Deep-EIoU on SportsMOT and SoccerNet, reporting large improvements in HOTA, AssA, IDF1, and reductions in ID switches, including HOTA of 81.04% on SportsMOT and 83.11% on SoccerNet. The code is open-sourced.

Significance. If the reported results hold, GTA would be a simple, reusable, post-hoc refinement tool with consistent benefits across multiple trackers and two sports datasets, which is practically valuable. The paper's strengths include the clear two-stage design, the consistency of the improvements across SORT, ByteTrack, and Deep-EIoU, and the open-source release. The main uncertainties are the provenance of the ReID model used for all appearance comparisons and the lack of sensitivity analysis for the five hyperparameters, which together affect the verifiability and generalizability of the central claims. The state-of-the-art claim also depends on comparisons with existing global-link methods that are currently missing.

major comments (3)

[§4.2, ReID Model] The statement 'we use the OSNet model trained on SportsMOT dataset' does not specify whether this means the official SportsMOT training split. Since Table 1 reports results on the SportsMOT test set, a checkpoint trained on the full dataset or on sequences overlapping the test videos could encode test-time identities, directly inflating AssA, IDF1, HOTA, and the reported ID-switch reductions in Tables 1 and 2. The authors must state the exact split used for ReID training and confirm, for example by releasing the checkpoint and training script, that no SportsMOT test sequence was seen during ReID training; otherwise the central plug-and-play claim and the magnitude of the improvements are not verifiable.
[§4.2, Hyperparameters] Five free parameters (epsilon, s, k, alpha, beta) are fixed without sensitivity analysis, and beta is set differently per dataset (1 for SportsMOT, 0.7 for SoccerNet). Given that no error bars or per-sequence variance are reported, it is unclear how robust the gains are to reasonable perturbations of these choices, and whether the per-dataset beta is a principled setting or a form of test-set tuning. I request a sensitivity table varying each parameter around its chosen value, or at least results with a shared beta across both datasets.
[§2.3 and Tables 1-2] Prior global tracklet association methods (Translink, AFLink, Split and Connect, MambaTrack) are described in Related Work but are not compared against in the experiments. Because the abstract claims state-of-the-art performance, the absence of these baselines leaves the SOTA claim unsupported. Please add comparisons with at least one or two existing global-link methods under a shared ReID setting, or weaken the claim to describe improvements over the selected online trackers.

minor comments (6)

[§4.2, Tracker] The text says Deep-EIoU achieves 85.4% HOTA on the SoccerNet test set published in 2022, while Table 2 reports the Deep-EIoU baseline as 79.41%; please clarify which SoccerNet split/version is being used and why the numbers differ.
[§3.2, Eq. (1)] The notation reuses i and j for both tracklet indices and frame indices, and the summation is written over 'i∈Πi' and 'j∈Πj'; please use distinct frame indices (for example p and q) and clarify the temporal-overlap condition in the first case.
[§3.2, Eq. (4)] The spatial constraint is defined for 'beginning and ending' points of tracklets, but the text does not state whether the constraint is applied symmetrically or how it interacts with tracklets whose temporal spans overlap; please make this explicit.
[§4.3] The text says the method reduces ID switches and 'tracklet fragments (Frag)', but Frag is not reported in Tables 1 or 2; either include it or remove the mention.
[Table 3] The baseline rows are not labelled with the method name in the leftmost column, which makes the table hard to parse; please add explicit method names to those rows.
[Abstract and Conclusion] The abstract states 'consistently increasing the HOTA score from 79.41% to 83.11%' without identifying Deep-EIoU as the baseline; please name the baseline tracker and note that these are single-run results without error bars.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: GTA is an empirical post-processor whose gains are measured, not derived; the ambiguous SportsMOT training split for the OSNet ReID model is a data-integrity concern, not a circular step.

full rationale

The paper does not present a derivation chain in which a predicted quantity is reconstructed from its own inputs. GTA is an algorithmic post-processor: the Tracklet Splitter applies DBSCAN to OSNet embeddings, and the Tracklet Connector builds the cosine-distance matrix of Eq. (1), applies the spatial constraints of Eqs. (2)-(4), and runs hierarchical clustering. These equations define the procedure; they do not entail the reported HOTA, AssA, IDF1, or ID-switch numbers, which are computed from ground-truth annotations after running the pipeline. The hyperparameters (s=5, epsilon=0.6, k=3, alpha=0.4, beta=1/0.7) are manually chosen per dataset, not fitted parameters renamed as predictions; the metric values are not algebraic consequences of these choices. Self-citations ([18], [19], [25]) provide background and baselines from the same group, but the paper does not rely on a self-cited theorem or uniqueness result to force its design, and no ansatz is smuggled in via citation. The one concrete ambiguity is Section 4.2: 'For our experiments, we use the OSNet [32] model trained on SportsMOT dataset.' If this checkpoint was trained on the SportsMOT test videos rather than the official training split, the appearance embeddings in Eq. (1) would encode test identities and inflate the association gains; the manuscript does not state the split. That is a potential data-leakage/correctness risk, not a circularity of the method's derivation, and it cannot be established from the paper alone. Because the central claim is an empirical improvement and the method is self-contained against the reported baselines, the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a pretrained ReID model, clustering hyperparameters, and assumptions about sports camera geometry. These are inputs from prior literature or hand choices, not derived by the paper.

free parameters (5)

epsilon (DBSCAN max neighbor distance) = 0.6
Set in Section 4.2 without sensitivity analysis; controls cluster formation in the splitter.
s (DBSCAN minimum samples) = 5
Set in Section 4.2; influences which points start clusters in the splitter.
k (DBSCAN maximum clusters) = 3
Set in Section 4.2; limits how many fragments a tracklet can be split into.
alpha (merging threshold) = 0.4
Set in Section 4.2; controls when hierarchical clustering stops merging tracklets.
beta (spatial constraint factor) = 1 for SportsMOT, 0.7 for SoccerNet
Set separately per dataset in Section 4.2; directly changes which tracklet pairs are allowed to merge.

assumptions (4)

domain assumption DBSCAN with outlier reassignment and max clusters k yields correct identity segmentation within mixed tracklets.
The splitter's correctness is assumed; no validation of split purity against ground truth is presented.
domain assumption OSNet embeddings trained on SportsMOT provide reliable appearance similarity for sports players across datasets.
Both the splitter and connector trust cosine distances of these embeddings; no embedding quality analysis is given.
domain assumption Cameras are fixed and players never exit and re-enter from opposite field sides, so the spatial constraints in Eqs. 2-4 are valid.
Enforced in the connector; may not generalize to moving broadcast cameras or unusual field geometries.
domain assumption The ReID training set does not overlap the SportsMOT test videos used for evaluation.
The paper only says 'trained on SportsMOT dataset' in Section 4.2; the results assume no leakage.

how reviews work

0 comments

Cite this review

Pith. "Pith review of GTA: Global Tracklet Association for Multi-Object Tracking in Sports." pith.science (2026). https://pith.science/paper/FH23SMFC

@misc{pith2026241108216,
  author       = {Pith},
  title        = {Pith review of: GTA: Global Tracklet Association for Multi-Object Tracking in Sports},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FH23SMFC}},
  note         = {Machine review of arXiv:2411.08216}
}

read the original abstract

Multi-object tracking in sports scenarios has become one of the focal points in computer vision, experiencing significant advancements through the integration of deep learning techniques. Despite these breakthroughs, challenges remain, such as accurately re-identifying players upon re-entry into the scene and minimizing ID switches. In this paper, we propose an appearance-based global tracklet association algorithm designed to enhance tracking performance by splitting tracklets containing multiple identities and connecting tracklets seemingly from the same identity. This method can serve as a plug-and-play refinement tool for any multi-object tracker to further boost their performance. The proposed method achieved a new state-of-the-art performance on the SportsMOT dataset with HOTA score of 81.04%. Similarly, on the SoccerNet dataset, our method enhanced multiple trackers' performance, consistently increasing the HOTA score from 79.41% to 83.11%. These significant and consistent improvements across different trackers and datasets underscore our proposed method's potential impact on the application of sports player tracking. We open-source our project codebase at https://github.com/sjc042/gta-link.git.

Figures

Figures reproduced from arXiv: 2411.08216 by the authors.

**Figure 1.** Our proposed Global Tracklet Association (GTA) method significantly boosts the HOTA and IDF1 score of existing trackers, such as SORT, ByteTrack, and DeepEIoU, on sports tracking datasets, including SportsMOT and SoccerNet. algorithm in handling re-entry situation after leaving camera field of view after certain amount of time. Current state-of-the-art on-line tracking algorithms often fail in long-term object re-i… view at source ↗

**Figure 2.** Examples of different players on the same teams, highlighting the challenge of distinguishing between players with similar appearances in sports tracking. With progress made in object detection and tracking, recent studies have focused on challenging multi-object tracking (MOT) scenarios like sports [7,8] and dancing [24]. Tracking sports players is more difficult than tracking pedestrians due to the complex natur… view at source ↗

**Figure 3.** An example of a mix-up error in a single tracklet. The tracklet output by the online tracking system contains three different identities, represented by purple, green, and red points. The figure illustrates the tracklet’s features extracted by a ReID model and clustered using the DBSCAN clustering algorithm. the camera view. Several works [4,18,19,29] have proposed methods to handle irregular object motion, improvi… view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: An example of a cut-off error, where a player’s tracklet is fragmented into four separate segments due to the player exiting and re-entering the camera view multiple times throughout the video sequence. the data association stage, such as DeepSORT [26] and BoTSORT [1].…

**Figure 5.** Figure 5: Illustration of tracklet splitter. method is specifically designed to conduct player re-identification and boost the tracking performance in various sports scenarios. 3 Methods Drawing inspiration from global link models presented in recent works [18] [29], we propose …

**Figure 6.** Figure 6: Illustration of tracklet connector. packed together while marking points that lie alone in low-density regions as outliers. It is important to note that, unlike traditional DBSCAN implementations, our adapted version assigns outliers to the nearest clusters at the end…

**Figure 7.** Figure 7: The cosine distance matrix of the embeddings of three stages: (1) Before Split, (2) After Split, and (3) After Connect. Both the x-axis and y-axis represent the IDs, while the darker color represents the farther distance [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

**Figure 8.** Figure 8: Tracking visualization of athletes before and after applying Global Tracklet Association (GTA). In rows one and three, dashed lines indicate association errors, showing inconsistent athlete IDs across frames. In contrast, solid lines represent correct associations wit…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 24 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]

Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: The clear mot metrics. J. Image Video Process.2008 (2008)

work page 2008
[3]

In: 2016 IEEE International Conference on Image Processing (ICIP)

Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP). pp. 3464–3468 (2016). https://doi.org/10.1109/ICIP.2016.7533003

arXiv 2016
[4]

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
[5]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Cherdchusakulchai, R., Phimsiri, S., Trairattanapa, V., Tungjitnob, S., Kudis- thalert, W., Kiawjak, P., Thamwiwatthana, E., Borisuitsawat, P., Tosawadi, T., Choppradi, P., et al.: Online multi-camera people tracking with spatial-temporal mechanism and anchor-feature hierarchical clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision an...

work page 2024
[6]

In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)

Cioppa, A., Deliège, A., Giancola, S., Ghanem, B., Van Droogenbroeck, M., Gade, R., Moeslund, T.B.: A context-aware loss function for action spotting in soccer videos. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)

work page 2020
[7]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Cioppa, A., Giancola, S., Deliege, A., Kang, L., Zhou, X., Cheng, Z., Ghanem, B., Van Droogenbroeck, M.: Soccernet-tracking: Multiple object tracking dataset and benchmark in soccer videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3491–3502 (2022)

work page 2022
[8]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Cui, Y., Zeng, C., Zhao, X., Yang, Y., Wu, G., Wang, L.: Sportsmot: A large multi- object tracking dataset in multiple sports scenes. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9921–9931 (2023)

work page 2023

Show all 32 references

[9]

IEEE Transactions on Multimedia (2023)

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

2023
[10]

In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Duan, H., Zhao, Y., Chen, K., Lin, D., Dai, B.: Revisiting skeleton-based action recognition. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (Jun 2022).https://doi.org/10.1109/cvpr52688. 2022.00298, http://dx.doi.org/10.1109/CVPR52688.2022.00298

2022
[11]

In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96)

Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discov- ering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). pp. 226–231. AAAI Press (1996)

1996
[12]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2020)

Gade, R., Moeslund, T.B.: Constrained multi-target tracking for team sports ac- tivities. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2020)

2020
[13]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops

Gutiérrez-Pérez, M., Agudo, A.: No bells just whistles: Sports field registration by leveraging geometric properties. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. pp. 3325–3334 (June 2024)

2024
[14]

In: CVPR workshops

Hsu, H.M., Huang, T.W., Wang, G., Cai, J., Lei, Z., Hwang, J.N.: Multi-camera tracking of vehicles based on deep features re-id and trajectory-based camera link models. In: CVPR workshops. pp. 416–424 (2019) GTA: Global Tracklet Association for Multi-Object Tracking in Sports 13

2019
[15]

arXiv preprint arXiv:2403.10826 (2024)

Huang, H.W., Yang, C.Y., Chai, W., Jiang, Z., Hwang, J.N.: Exploring learning- based motion models in multi-object tracking. arXiv preprint arXiv:2403.10826 (2024)

2024 arXiv
[16]

arXiv preprint arXiv:2301.07805 (2023)

Huang, H.W., Yang, C.Y., Hwang, J.N.: Multi-target multi-camera vehicle track- ing using transformer-based camera link model and spatial-temporal information. arXiv preprint arXiv:2301.07805 (2023)

2023 arXiv
[17]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops

Huang, H.W., Yang, C.Y., Jiang, Z., Kim, P.K., Lee, K., Kim, K., Ramkumar, S., Mullapudi, C., Jang, I.S., Huang, C.I., Hwang, J.N.: Enhancing multi-camera people tracking with anchor-guided clustering and spatio-temporal consistency id re-assignment. In: Proceedings of the IEE...

2023
[18]

In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

Huang, H.W., Yang, C.Y., Ramkumar, S., Huang, C.I., Hwang, J.N., Kim, P.K., Lee, K., Kim, K.: Observation centric and central distance recovery for athlete tracking. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 454–460 (2023)

2023
[19]

In: Proceedings of the IEEE/CVF Winter Con- ference on Applications of Computer Vision

Huang, H.W., Yang, C.Y., Sun, J., Kim, P.K., Kim, K.J., Lee, K., Huang, C.I., Hwang, J.N.: Iterative scale-up expansioniou and deep features association for multi-object tracking in sports. In: Proceedings of the IEEE/CVF Winter Con- ference on Applications of Computer Vision....

2024
[20]

In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Jiang, W., Higuera, J.C.G., Angles, B., Sun, W., Javan, M., Yi, K.M.: Optimizing through learned errors for accurate sports field registration. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2020)

2020
[21]

In: 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

Jiang, Z., Ji, H., Menaker, S., Hwang, J.N.: Golfpose: Golf swing analyses with a monocular camera based human pose estimation. In: 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). pp. 1–6. IEEE (2022)

2022
[22]

International journal of computer vision129, 548–578 (2021)

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)

2021
[23]

Milan, A., Leal-Taixe, L., Reid, I., Roth, S., Schindler, K.: Mot16: A benchmark for multi-object tracking (2016), arXiv preprint arXiv:1603.00831

2016 arXiv
[24]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Sun, P., Cao, J., Jiang, Y., Yuan, Z., Bai, S., Kitani, K., Luo, P.: Dancetrack: Multi-object tracking in uniform appearance and diverse motion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20993–21002 (2022)

2022
[25]

IEEE Transactions on Multimedia25, 1256–1268 (2022)

Wang, G., Wang, Y., Gu, R., Hu, W., Hwang, J.N.: Split and connect: A universal tracklet booster for multi-object tracking. IEEE Transactions on Multimedia25, 1256–1268 (2022)

2022
[26]

Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric (2017), in 2017 IEEE international conference on image processing(ICIP), pages 3645–3649. IEEE

2017
[27]

In: Proceedings of the IEEE/CVF Winter Conference on Appli- cations of Computer Vision (WACV) Workshops

Yang, C.Y., Huang, H.W., Jiang, Z., Kuo, H.C., Mei, J., Huang, C.I., Hwang, J.N.: Sea you later: Metadata-guided long-term re-identification for uav-based multi- object tracking. In: Proceedings of the IEEE/CVF Winter Conference on Appli- cations of Computer Vision (WACV) Work...

2024
[28]

In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition

Yang, C.Y., Huang, H.W., Kim, P.K., Jiang, Z., Kim, K.J., Huang, C.I., Du, H., Hwang, J.N.: An online approach and evaluation method for tracking people across cameras in extremely long video sequence. In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern...

2024
[29]

Yang, F., Odashima, S., Masui, S., Jiang, S.: Hard to track objects with irregular motions and similar appearances? make it easier by buffering the matching space. 14 J. Sun et al. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 4799–4...

2023
[30]

In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Zhang, Y., Wang, S., Fan, Y., Wang, G., Yan, C.: Translink: Transformer-based embedding for tracklets’ global link. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1–5. IEEE (2023)

2023
[31]

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
[32]

Zhou,K.,Yang,Y.,Cavallaro,A.,Xiang,T.:Omni-scalefeaturelearningforperson re-identification (2019), proceedings of the IEEE/CVF International Conference on Computer Vision

2019

Pith tools

Reviewed August 12, 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]

Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: The clear mot metrics. J. Image Video Process.2008 (2008)

work page 2008

[3] [3]

In: 2016 IEEE International Conference on Image Processing (ICIP)

Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP). pp. 3464–3468 (2016). https://doi.org/10.1109/ICIP.2016.7533003

arXiv 2016

[4] [4]

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

[5] [5]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Cherdchusakulchai, R., Phimsiri, S., Trairattanapa, V., Tungjitnob, S., Kudis- thalert, W., Kiawjak, P., Thamwiwatthana, E., Borisuitsawat, P., Tosawadi, T., Choppradi, P., et al.: Online multi-camera people tracking with spatial-temporal mechanism and anchor-feature hierarchical clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision an...

work page 2024

[6] [6]

In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)

Cioppa, A., Deliège, A., Giancola, S., Ghanem, B., Van Droogenbroeck, M., Gade, R., Moeslund, T.B.: A context-aware loss function for action spotting in soccer videos. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)

work page 2020

[7] [7]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Cioppa, A., Giancola, S., Deliege, A., Kang, L., Zhou, X., Cheng, Z., Ghanem, B., Van Droogenbroeck, M.: Soccernet-tracking: Multiple object tracking dataset and benchmark in soccer videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3491–3502 (2022)

work page 2022

[8] [8]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Cui, Y., Zeng, C., Zhao, X., Yang, Y., Wu, G., Wang, L.: Sportsmot: A large multi- object tracking dataset in multiple sports scenes. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9921–9931 (2023)

work page 2023