Pith. sign in

REVIEW 5 major objections 6 minor 24 references

A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper argues that two-stage hyperparameter tuning alone lets a simple SORT tracker outperform more complex re-identification and diffusion-based trackers on thermal multi-object tracking.

desk verdict A useful challenge report with a real empirical win, overpackaged as a 'novel tuning method' that is actually one-at-a-time grid search on the evaluation set. read the letter →

arxiv 2507.02408 v1 pith:C65C573J submitted 2025-07-03 cs.CV

Duong Nguyen-Ngoc Tran , Long Hoang Pham , Chi Dai Tran , Quoc Pham-Nam Ho , Huy-Hung Nguyen , Jae Wook Jeon This is my paper
classification cs.CV
keywords multi-objecttrackingthermalimaginghyperparametertuningpedestrianSORTtrackerYOLOv8detectorsurveillancePBVSMOT
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 tries to show that for thermal multi-object tracking, the choice of hyperparameters at the detection and association stages matters more than the complexity of the tracker itself. Within the TP-MOT challenge's restriction to YOLOv8s as the detector, it tunes detector settings (training and inference image size, NMS threshold, confidence threshold) before tracker settings (track age, minimum hits, association cost), one parameter at a time, using the onsite evaluation set. The tuned SORT tracker reaches a tracking-accuracy score (MOTA) of 98.4 on the evaluation server, and the framework is reported as the challenge winner with a weighted result of 0.71. If the claim is right, high-accuracy thermal surveillance tracking can be built from a simple Kalman-filter tracker plus careful tuning, without expensive re-identification or learned motion models.

What carries the argument

The central mechanism is the two-stage tuning loop. Stage 1 updates detector hyperparameters, namely training image size, inference image size, non-maximum suppression threshold, and confidence threshold, while Stage 2 updates tracker hyperparameters, namely track age, minimum hits, association cost, motion model, and association algorithm. Each update is a small additive change, the tracking metrics are re-evaluated on the onsite set, and the change is kept or reset depending on whether the metric improves. This loop, rather than any new network or motion model, is what the paper credits for the accuracy gain.

What would settle it

Hold out several thermal sequences from a camera location not used in tuning, run the tuned SORT configuration on them, and compare with default SORT and a re-identification tracker; if the tuned configuration's MOTA advantage disappears or reverses on the held-out scenes, the claim that the tuned parameters generalize is false.

Watch

Extended reading notes

Core claim

The paper's central claim is that a plain SORT tracker, whose Kalman filter and Hungarian association are left unchanged, becomes the best-performing method on the PBVS Thermal MOT benchmark once its detection and tracking hyperparameters are tuned in sequence. The reported numbers are MOTA 98.4 on the evaluation server, with the framework ranked first in the 1st Thermal Pedestrian Multiple Object Tracking Challenge, ahead of ByteTrack, BoT-SORT, BoostTrack, and DiffMOT. The paper reads this as evidence that thermal MOT difficulty lies in detector calibration and association thresholds, not in the need for re-identification features or diffusion-based motion prediction. Because the challenge fixed the detector family to YOLOv8s, the comparison isolates the effect of tuning rather than architecture choice.

Load-bearing premise

The approach assumes the best value for each hyperparameter can be found by changing one parameter at a time and that the values chosen on the onsite evaluation set will also be best for other thermal camera scenes; if either assumption fails, the reported accuracy gain will not transfer.

Editorial extensions

If this is right

  • A tracker as simple as SORT, with tuned detector and association thresholds, can exceed the accuracy of diffusion-based and re-identification trackers on thermal pedestrian data.
  • Detection-stage settings are a primary lever for thermal tracking accuracy; image size, NMS, and confidence threshold changes shift MOTA and identity metrics substantially in the reported tables.
  • The framework can be deployed with low computational overhead because it adds no learned re-identification or motion-prediction components.
  • The winning challenge result provides a practical recipe for thermal surveillance systems: tune the detector to the scene before considering a more complex tracker.

Reading between the lines

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

  • If the one-at-a-time independence assumption holds up, the same tuning recipe could be transferred to other thermal cameras by re-running the short grid on a per-site validation clip, which the paper does not test.
  • A joint search that varies two parameters at once, such as confidence threshold and association cost, would reveal whether the manual loop leaves interaction effects unexplored; the paper's tables only vary one parameter at a time.
  • The comparison set suggests that detector calibration may dominate association complexity in thermal MOT; a corresponding test on RGB MOT would show whether that is a thermal-specific property or a general one.
  • The real-time claim is built on using lightweight components; measuring frames-per-second on the stated desktop hardware would turn that design inference into a verified property.
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.

Referee Report

5 major / 6 minor

Summary. The paper proposes a two-stage hyperparameter tuning framework for multi-object tracking (MOT) in thermal imagery. Detection (YOLOv8s) and tracking (SORT) hyperparameters are adjusted based on evaluation results, with the goal of achieving high tracking accuracy without complex re-identification or motion models. The method is evaluated on the PBVS Thermal MOT dataset, where the authors report winning the 1st Thermal Pedestrian Multiple Object Tracking Challenge (TP-MOT). Comparisons are made against ByteTrack, BoTTrack, BoostTrack, and DiffMOT. The paper claims real-time performance and robustness across thermal camera conditions.

Significance. If the methodological claims were fully specified and validated, the paper would provide a practical demonstration that careful hyperparameter tuning of a simple tracker (SORT) can rival or exceed more complex approaches on thermal MOT. The public code release and the reported challenge win are useful community resources. However, as written, the tuning method is not actually defined, the evaluation protocol is susceptible to selection bias, and internal inconsistencies in the reported numbers prevent a reliable assessment. The significance is therefore currently limited; the paper is more of a challenge report than a validated methodological contribution.

major comments (5)
  1. [Section 3, Eqs. (4) and (7)] The central methodological claim rests on update equations of the form θ^k = θ^{k-1} + Δθ, but the increments Δd_train, Δd_infer, Δd_NMS, Δd_conf, Δt_mm, Δt_cost, Δt_assoc, Δt_min-hit, and Δt_age are never defined. No objective function, search strategy, or stopping rule is given. Section 4.5 then describes manual one-at-a-time sweeps over these hyperparameters, which is standard grid search rather than a 'novel tuning method.' The authors need to either specify an automated tuning loop with well-defined increments and a convergence criterion, or explicitly position the contribution as an empirical study of hyperparameter sensitivity.
  2. [Section 4.5, Tables 2–8] All hyperparameter choices are selected using the onsite evaluation set, and the same set is used for the 'Evaluation - Onsite' results in Table 8. There is no separate validation split or cross-validation. Moreover, the one-at-a-time tuning procedure assumes each hyperparameter can be optimized independently, but parameters such as d_conf and d_NMS interact (e.g., lower confidence thresholds increase the number of candidate detections that NMS must suppress). As a result, the paper's generalization claims across different thermal camera conditions are unsupported. A held-out validation set or at least an analysis of parameter interactions is required.
  3. [Section 4.6, Table 8] The text states that 'SORT achieves the highest MOTA (93.77) and IDF1 (80.78),' but Table 8 reports SORT with MOTA 98.8 (onsite) and 98.4357 (server), and IDF1 84.3/81.3. The numbers 93.77 and 80.78 instead match Table 5 (tage = 40). Additionally, MOTP is given as 87.3 in the onsite block and 0.126381 in the server block, apparently on different scales. This inconsistency makes it unclear which values are the official results and prevents verification of the reported ranking.
  4. [Section 4.1, throughout] The paper repeatedly claims real-time processing capability, but no runtime, FPS, or latency measurements are provided anywhere. The hardware setup (Section 4.1) is described, yet no timing results are reported for SORT or the comparison trackers. Without quantitative runtime data, the real-time claim is unsupported.
  5. [Section 4.5/Table 8] The comparison in Table 8 is not controlled: SORT's hyperparameters are tuned on the onsite evaluation set, while ByteTrack, BoTTrack, BoostTrack, and DiffMOT appear to be used with default or pretrained settings. If SORT's advantage is due to test-set overfitting, the comparison does not demonstrate the superiority of the proposed tuning approach. The server results mitigate this concern to some degree, but the paper should clarify the exact protocol used for each baseline and whether any baseline tuning was performed.
minor comments (6)
  1. [Tables 5–7] Tables 5, 6, and 7 all share the same caption 'The result of tunning in age of tracklet,' but Table 6 concerns t_min-hit and Table 7 concerns t_cost. Please correct the captions.
  2. [Section 3.2, Eq. (5)] Equation (5) describes a tracklet of 'the vehicle,' but the task is pedestrian tracking; the terminology should be consistent.
  3. [Throughout] There are numerous typographical errors, including 'Tunning' in Figure 1 and table captions, 'valiation' in Section 3.2, 'Therfore' in Section 3.2, and 'mention above' in Section 3.2. A careful proofreading pass is needed.
  4. [Section 2.1] The discussion of YOLOv9 and YOLOv11 is tangential and includes vague statements (e.g., 'YOLOv11 can be interpreted as a representation of the YOLO framework's continuous advancement'). This paragraph could be shortened or removed.
  5. [Section 4.3] The description of renaming image files to ensure correct frame order is an implementation detail that may be better placed in supplementary material or a footnote.
  6. [Section 4.2, Table 1] The MOTAL metric is listed in Table 1 but is not defined in Section 4.2; please add its formula or a reference.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the 'tuning method' is an underspecified grid search, the selected best values are argmax over the sweep tables by construction, and the challenge ranking is external evidence; methodological risks (selection bias, no held-out validation) are not definitional circularity.

full rationale

This paper contains no derivation chain from which a quantity is predicted, so there is little for circularity to attach to. The claimed 'novel tuning method' is formalized only as the placeholder update rules in Eqs. (4) and (7) (e.g., 'dk_train = dk-1_train + Δdtrain'), where every Δ is undefined and no objective, stopping rule, or automated loop is given; these equations are vacuous rather than a reduction of an output to an input. The actual procedure in Section 4.5 (Tables 2-7) is a standard one-at-a-time grid search whose 'optimal' hyperparameter values are, by construction, the rows with the best reported metric on the onsite evaluation set; the paper does not present those in-sample argmax values as an independent prediction, so the fitted-input-called-prediction pattern does not apply. Table 8 additionally reports out-of-sample evaluation-server results (SORT MOTA 98.4) and Table 9 reports an externally adjudicated first-place challenge ranking, both of which are genuine external evidence. There is no self-citation at all: all 23 references are to external works (SORT, ByteTrack, DiffMOT, YOLO, the TP-MOT challenge paper), so no self-citation chain, no imported uniqueness theorem, and no ansatz-smuggled-via-citation pattern exists. The legitimate concerns — tuning and reporting on the same onsite set (selection bias), no held-out validation or interaction analysis, no runtime measurements supporting the real-time claim, and the unproven generalization to 'various thermal camera conditions' — are correctness and evidence-quality risks, not constructional circularity. No equation in the paper reduces to its own input and no fitted parameter is renamed as a prediction, so the honest verdict is no significant circularity (score 1 reflecting the weak in-sample selection flavor, which is outside the circularity definitions used here).

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The central empirical claim rests on a set of hand-tuned hyperparameters for YOLOv8s and SORT. The one-factor-at-a-time tuning procedure assumes parameter independence and uses the onsite evaluation set as the guide, with no separate validation partition. No new entities are introduced; the only invented conceptual piece is the 'tuning framework' itself, which is not algorithmically specified.

free parameters (7)
  • dtrain (training image size) = 1600
    Selected by grid search over the onsite evaluation set (Table 4); no separate validation partition.
  • dinfer (inference image size) = 1600
    Selected by grid search over the onsite evaluation set (Table 4).
  • dNMS (NMS threshold) = 0.75
    Selected by grid search over the onsite evaluation set (Table 2).
  • dconf (confidence threshold) = 0.0001
    Selected by grid search over the onsite evaluation set (Table 3).
  • tage (max track age) = 40
    Selected by grid search over the onsite evaluation set (Table 5).
  • tmin-hit (minimum hits for track initiation) = 3
    Selected by grid search over the onsite evaluation set (Table 6).
  • tcost (association cost threshold) = 0.01
    Selected by grid search over the onsite evaluation set (Table 7).
assumptions (4)
  • domain assumption Kalman filter linear motion model adequately captures pedestrian motion in thermal sequences.
    SORT is built on a linear Kalman filter; the paper does not test this assumption for thermal data despite the title claiming 'complexity motion pattern'.
  • ad hoc to paper Each hyperparameter can be tuned independently while holding others fixed.
    Section 4.5 sweeps one parameter at a time; no joint interaction is considered, and Equations (4) and (7) provide no cross-parameter update.
  • ad hoc to paper The onsite evaluation set is an unbiased guide for choosing final hyperparameters.
    Tables 5-7 tune on 'Evaluation - Onsite' with no separate validation set, so final reported results may be optimistically selected.
  • domain assumption The challenge metrics MOTA, MOTP, and IDF1 fully capture tracking quality.
    The ranking is based on these metrics, which are standard but not a complete measure of real-world tracking performance.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern." pith.science (2026). https://pith.science/paper/C65C573J

@misc{pith2026250702408,
  author       = {Pith},
  title        = {Pith review of: A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C65C573J}},
  note         = {Machine review of arXiv:2507.02408}
}
read the original abstract

Multi-Object Tracking in thermal images is essential for surveillance systems, particularly in challenging environments where RGB cameras struggle due to low visibility or poor lighting conditions. Thermal sensors enhance recognition tasks by capturing infrared signatures, but a major challenge is their low-level feature representation, which makes it difficult to accurately detect and track pedestrians. To address this, the paper introduces a novel tuning method for pedestrian tracking, specifically designed to handle the complex motion patterns in thermal imagery. The proposed framework optimizes two-stages, ensuring that each stage is tuned with the most suitable hyperparameters to maximize tracking performance. By fine-tuning hyperparameters for real-time tracking, the method achieves high accuracy without relying on complex reidentification or motion models. Extensive experiments on PBVS Thermal MOT dataset demonstrate that the approach is highly effective across various thermal camera conditions, making it a robust solution for real-world surveillance applications.

Figures

Figures reproduced from arXiv: 2507.02408 by the authors.

Figure 1
Figure 1. Comparison between typical two-stage multi-object [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Using the thermal sensor input, Stage 1 involves running the detector to identify each object’s class, location, and confidence [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The visualization of result of SORT, ByteTrack, BoostTrack, BoTSORT, and DiffMOT. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

24 extracted references · 19 canonical work pages

  1. [1]

    BoT- SORT: Robust Associations Multi-Pedestrian Tracking,

    Nir Aharon, Roy Orfaig, and Ben-Zion Bobrovsky. BoT- SORT: Robust Associations Multi-Pedestrian Tracking,

    work page

  2. [2]

    Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Mo- tion Similarity, 2024

    Wassim El Ahmar, Dhanvin Kolhatkar, Farzan Nowruzi, and Robert Laganiere. Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Mo- tion Similarity, 2024. 3

    work page 2024

  3. [3]

    Simple online and realtime tracking

    Alex Bewley, Zongyuan Ge, Lionel Ott, Fabio Ramos, and Ben Upcroft. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), pages 3464–3468, Phoenix, AZ, USA, 2016. IEEE. 3

    work page 2016

  4. [4]

    YOLOv4: Optimal Speed and Accuracy of Object Detection

    Alexey Bochkovskiy, Chien-Yao Wang, and Hong- Yuan Mark Liao. YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv:2004.10934 [cs, eess] , 2020. arXiv: 2004.10934. 3

    arXiv 2004

  5. [5]

    Ther- mal pedestrian multiple object tracking challenge (tp-mot)

    Wassim El Ahmar, Angel Sappa, and Riad Hammoud. Ther- mal pedestrian multiple object tracking challenge (tp-mot). In Proceedings of the Computer Vision and Pattern Recog- nition Conference (CVPR) Workshops , pages 4602–4609,

    work page

  6. [6]

    Fast R-CNN

    Ross Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440–1448, Santiago, Chile, 2015. IEEE. 2

    work page 2015

  7. [7]

    Rich Feature Hierarchies for Accurate Object Detec- tion and Semantic Segmentation

    Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich Feature Hierarchies for Accurate Object Detec- tion and Semantic Segmentation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition , pages 580– 587, Columbus, OH, USA, 2014. IEEE. 2

    work page 2014

  8. [8]

    Multispectral pedestrian detection: Benchmark dataset and baseline

    Soonmin Hwang, Jaesik Park, Namil Kim, Yukyung Choi, and In So Kweon. Multispectral pedestrian detection: Benchmark dataset and baseline. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1037–1045, Boston, MA, USA, 2015. IEEE. 3, 6

    work page 2015

Show all 24 references
  1. [9]

    Ultralytics YOLO, 2023

    Glenn Jocher, Jing Qiu, and Ayush Chaurasia. Ultralytics YOLO, 2023. 3

    2023

  2. [10]

    R. E. Kalman. A New Approach to Linear Filtering and Pre- diction Problems. Journal of Basic Engineering , 82(1):35– 45, 1960. 5

    1960

  3. [11]

    YOLOv6: A Single-Stage Ob- ject Detection Framework for Industrial Applications, 2022

    Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, Yiduo Li, Bo Zhang, Yufei Liang, Linyuan Zhou, Xiaoming Xu, Xiangxiang Chu, Xiaoming Wei, and Xiaolin Wei. YOLOv6: A Single-Stage Ob- ject Detection Framew...

    2022 arXiv

  4. [12]

    PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

    Qiao Liu, Zhenyu He, Xin Li, and Yuan Zheng. PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark. IEEE Transactions on Multimedia, 22(3):666–675, 2020. 3, 6

    2020

  5. [13]

    Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. SSD: Single Shot MultiBox Detector. In Computer Vision – ECCV 2016 , pages 21–37. Springer International Publishing, Cham, 2016. Series Title: Lecture Notes in Com- ...

    2016

  6. [14]

    DiffMOT: A Real-time Diffusion- based Multiple Object Tracker with Non-linear Prediction

    Weiyi Lv, Yuhang Huang, Ning Zhang, Ruei-Sung Lin, Mei Han, and Dan Zeng. DiffMOT: A Real-time Diffusion- based Multiple Object Tracker with Non-linear Prediction. In 2024 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 19321–19330, Seattle, W A,...

    2024

  7. [15]

    YOLOv3: An Incremen- tal Improvement

    Joseph Redmon and Ali Farhadi. YOLOv3: An Incremen- tal Improvement. arXiv:1804.02767 [cs] , 2018. arXiv: 1804.02767. 3

    2018 arXiv

  8. [16]

    You Only Look Once: Unified, Real-Time Object Detection

    Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You Only Look Once: Unified, Real-Time Object Detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788, Las Vegas, NV , USA, 2016. IEEE. 2, 3

    2016

  9. [17]

    Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137–1149, 2017. 2

    2017

  10. [18]

    Stanojevic and Branimir T

    Vukasin D. Stanojevic and Branimir T. Todorovic. Boost- Track: boosting the similarity measure and detection confi- dence for improved multiple object tracking.Machine Vision and Applications, 35(3):53, 2024. 3

    2024

  11. [19]

    BoostTrack++: using tracklet information to detect more objects in multiple object tracking, 2024

    Vuka ˇsin Stanojevi´c and Branimir Todorovi´c. BoostTrack++: using tracklet information to detect more objects in multiple object tracking, 2024. Version Number: 1. 3

    2024

  12. [20]

    YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors

    Chien-Yao Wang, Alexey Bochkovskiy, and Hong- Yuan Mark Liao. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7464–7475, Vancouver, BC, Canada, 2023...

    2023

  13. [21]

    YOLOv9: Learning What You Want to Learn Using Pro- grammable Gradient Information

    Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao. YOLOv9: Learning What You Want to Learn Using Pro- grammable Gradient Information. In Computer Vision – ECCV 2024 , pages 1–21. Springer Nature Switzerland, Cham, 2025. Series Title: Lecture Notes in Computer Sci- ence. 3

    2024

  14. [22]

    Simple online and realtime tracking with a deep association metric

    Nicolai Wojke, Alex Bewley, and Dietrich Paulus. Simple online and realtime tracking with a deep association metric. In 2017 IEEE International Conference on Image Processing (ICIP), pages 3645–3649, Beijing, 2017. IEEE. 3

    2017

  15. [23]

    ByteTrack: Multi-object Tracking by Associating Ev- ery Detection Box

    Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Fucheng Weng, Zehuan Yuan, Ping Luo, Wenyu Liu, and Xinggang Wang. ByteTrack: Multi-object Tracking by Associating Ev- ery Detection Box. In Computer Vision – ECCV 2022, pages 1–21. Springer Nature Switzerland, Cham, 2022. Series T...

    2022

  16. [2022]

    arXiv:2206.14651 [cs]. 3

    arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.