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REVIEW 4 major objections 5 minor 63 references

Effects of Blur and Deblurring to Visual Object Tracking

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Motion blur is not uniformly bad for trackers: light blur aids many, heavy blur hurts, and a learned gate that selectively deblurs improves six existing trackers.

desk verdict Useful controlled blur benchmark and a smart deblurring gate, but the ground-truth rule for blurred frames may be biasing the central 'heavy blur hurts' finding. read the letter →

arxiv 1908.07904 v1 pith:LNRVRXYJ submitted 2019-08-21 cs.CV

Qing Guo , Wei Feng , Zhihao Chen , Ruijun Gao , Liang Wan , Song Wang This is my paper · ORCID
classification cs.CV
keywords motionblurvisualobjecttrackingbenchmarkdeblurringGANrobustnessselectiveDeblur
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 effect of motion blur on visual object tracking is not monotonic: light blur often helps, heavy blur always hurts. To show this, it builds the Blurred Video Tracking (BVT) benchmark, 500 videos from 100 scenes at five blur levels produced by averaging successive 240 fps frames, with ground truth from averaged annotations. Twenty-three trackers are evaluated on this benchmark. The paper then shows that applying deblurring to every frame helps only on heavily blurred videos and hurts on lightly blurred ones. Its proposed scheme, DeblurGAN-D, fine-tunes the DeblurGAN discriminator to measure blur level and selectively deblur frames, which improves the accuracy and robustness of six trackers.

What carries the argument

The load-bearing objects are the benchmark construction and the blur assessor. Blurred videos are generated by averaging $L$ successive frames from 240 fps NfS videos ($L=1,2,4,8,16$), then temporally sampling every 8 frames to 30 fps; ground-truth boxes are the average of the annotations of the medium frames in each averaging window. The assessor is the DeblurGAN discriminator, fine-tuned on blur/deblur pairs from 20 benchmark scenes with the generator fixed, so it can rank blur levels. Its difference in scores between a deblurred and raw frame, $|D(\hat{I}_t) - D(I_t)| > \theta = 2.5$, triggers selective deblurring inside a Bayesian formulation with a selector variable.

What would settle it

Run a suite of trackers on real blurred video recorded with a paired high-speed camera, so the true sharp target position is known at the blurred frames' exposure midpoint. If light real blur never raises AUC above the sharp baseline, or if the DeblurGAN-D gate does not select deblurred frames more often on heavy blur, the central claims are contradicted. A simpler check: replace the averaged-annotation ground truth with manually labeled blurred boxes on the BVT videos and see whether the benchmark conclusions change.

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Extended reading notes

Core claim

The central claim is that tracker robustness to motion blur is level-dependent and that deblurring should be gated by a learned blur assessor rather than applied blindly. On the BVT benchmark, 17 of 23 trackers gain AUC on the lightest blurred subset and 14 on the second-lightest, while nearly all lose accuracy on the heaviest. Full-frame deblurring with DeblurGAN or SRN lowers accuracy on light blur and raises it on heavy blur. The paper's method treats the fine-tuned discriminator's score difference $D(\hat{I}_t) - D(I_t)$ as a blur-level signal; when it exceeds a threshold, the deblurred search region is used, otherwise the raw frame is kept. This selective scheme improves 6 of 7 tested trackers, with relative gains up to 9.3% for BT.

Load-bearing premise

The benchmark's ground truth assumes that averaging the annotations of the sharp frames inside an exposure window gives the correct bounding box for the averaged blurred frame, and that averaging L successive sharp frames faithfully reproduces real motion blur; if either fails, the measured blur effects and the six-tracker improvement may not transfer to real blurred video.

Editorial extensions

If this is right

  • Tracker rankings on blur subsets are incomplete unless the blur level is controlled; a tracker can be best on sharp frames and worse than others under heavy blur.
  • Light blur acts as a mild regularizer or augmentation for many trackers, so blur robustness should be reported across levels rather than as a single score.
  • Full-frame deblurring is the wrong default; deblurring should be applied only when the assessor indicates heavy blur.
  • Existing trackers can be upgraded without retraining them, by wrapping them with the selective deblurring scheme.
  • The normalized robustness score (NRS) gives a blur-robustness measure that is separated from absolute accuracy.

Reading between the lines

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

  • If light blur genuinely helps, a testable extension is to intentionally add small synthetic blur during tracker training or template augmentation; the paper's data suggests this could improve sharp-video accuracy for some trackers.
  • The same discriminator-difference gate could apply to other video tasks such as detection or segmentation, where heavy blur also corrupts features but light blur may not.
  • Temporal averaging of high-frame-rate frames may not capture all real camera blur, such as rolling-shutter or spatially varying kernels; validating the gate on real blurred video with paired sharp references would strengthen the transfer.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper builds a Blurred Video Tracking (BVT) benchmark by temporally averaging frames from the 240 fps NfS dataset at five blur levels (L = 1, 2, 4, 8, 16), with ground-truth boxes defined by averaging the annotations of the averaged frames. Twenty-three trackers are evaluated, leading to the claims that light blur often improves tracking accuracy while heavy blur always hurts it, and that full deblurring helps on heavily blurred videos but hurts on lightly blurred ones. The paper also proposes a blur-robust tracking scheme, DeblurGAN-D, which uses a fine-tuned discriminator as a blur assessor to selectively deblur frames, and reports accuracy improvements for six of seven tested trackers.

Significance. If the empirical findings are valid, the paper makes a useful contribution: it provides a controlled benchmark for blur robustness, documents a non-monotonic effect of blur level on tracker accuracy, and proposes a lightweight, tracker-agnostic gating mechanism for deblurring. The breadth of the evaluation (23 trackers, 4 feature families, 500 videos) and the reproducibility-friendly design (built on the public NfS dataset) are strengths. The paper also honestly identifies limitations of prior benchmarks. However, the central quantitative conclusions rest on a specific ground-truth generation rule for blurred frames, and several evaluation protocols are oracle-based or lack statistical safeguards; these issues must be resolved before the benchmark's findings can be taken as established.

major comments (4)
  1. [3.1 (Dataset)] The ground-truth rule for blurred frames is not geometrically correct for moving targets. The blurred frame is defined as \tilde I_t^L = avg(I_t,...,I_{t+L-1}) and the ground truth is 'the average of annotations of medium frames.' For a target that moves by d pixels during the exposure window, the visible smear occupies width w+d, while the averaged box has width approximately w and a centered midpoint. A tracker that perfectly predicts the true blurred target extent would receive IoU roughly w/(w+d), which decreases as L increases. Consequently, the AUC declines on S8/S16 and the conclusion 'heavy blur always hurts' may reflect the ground-truth rule rather than genuine tracker failure. The NRS metric inherits this bias because it gates frames using the IoU > 0.5 condition from S1 and then applies that gate to the undersized S{2,4,8,16} boxes. Please either justify the box-averaging rule with a physical model, use a union/coverage-based annotation for the integrated target, or validate the rule on human-annotated real blurred videos.
  2. [4.2 (Pros of selective deblurring)] The selective-deblurring results reported in Section 4.2 are oracle-based and cannot support the claim that selective deblurring 'improves the tracking performance of all trackers significantly.' The text states that 'the result with higher precision is saved as the final output,' where precision is computed from ground-truth center localization errors. This requires the ground truth at test time, so the gains in Figure 6 are an upper bound, not an achievable tracking algorithm. Please relabel this experiment as an oracle/upper-bound study and separate it from the achievable gains of the proposed DeblurGAN-D scheme in Section 5.3.
  3. [4 and 5 (Evaluation methodology)] No statistical significance tests or error bars are reported for any of the AUC comparisons. Claims such as '17 and 14 trackers have positive gains' on S2/S4 and 'light motion blur helps most of the compared trackers' are based on single benchmark runs; a paired bootstrap or Wilcoxon test across the 100/80 videos is needed to establish that these trends are not noise. The same applies to Section 5.4, where relative improvements (e.g., 9.3% for BT) are reported without variance or significance. Without such tests, the aggregate claims about 'most trackers' are not yet supported.
  4. [5.2 / 5.4 (Fine-tuning and evaluation split)] There is a distributional leakage concern in the validation of the proposed scheme. The DeblurGAN-D assessor is fine-tuned on 20 scenes of the BVT benchmark (80 blurred videos generated by the same frame-averaging procedure) and then evaluated on the remaining 80 scenes of the same benchmark, generated by the same averaging process. This demonstrates improvement on the synthetic blur distribution used for training, but it does not establish that the assessor generalizes to real camera motion blur or to deblurring artifacts from other generators. Please add an evaluation on real blurred videos (e.g., the OTB motion-blur subset or GoPro-captured sequences) and report sensitivity to the threshold theta = 2.5.
minor comments (5)
  1. [4.1] In the paragraph on AUC gains, the sentence 'Such numbers reduce to 7 and 2 on heavily-blurred subsets, i.e. S2 and S4' should refer to S8 and S16, not S2 and S4, which are the lightly blurred subsets.
  2. [4.1] The citation for BACF is incorrect: 'BACF [15]' should cite Galoogahi et al. 2017 (reference [16] in the bibliography), not the NfS benchmark [15].
  3. [4.2 / 5] The term 'full deburring' is used in several places; this appears to be a typo for 'full deblurring.' Please correct it for clarity.
  4. [5.2] The captions of Figures 8 and 9 mention 'bird sequences' and 'airplane sequences,' but the text does not identify which dataset or scenes these come from. Please clarify the source and the display convention (e.g., raw discriminator outputs vs. normalized scores).
  5. [5.3] Equation (4) defines P(I_t | s_t) as proportional to |D(hat I_t) - D(I_t)|, but this expression does not depend on s_t at all. Please specify how the two values of s_t (0 and 1) are represented in this likelihood, or revise the notation.

Circularity Check

1 steps flagged · score 3.0 of 10

Oracle-based selective deblurring in §4.2 improves by construction; final DeblurGAN-D scheme is independently validated.

  1. other [Section 4.2, 'Pros of selective deblurring' (paragraph introducing '*ganslt'/'*srnslt', around Fig. 6)]
    "We then predict the target position according to raw and deblurred frames respectively and obtain two bounding boxes whose center localization errors are calculated according to the ground truth. The result with higher precision is saved as the final output. ... Fig. 6 shows that selective deblurring via DeblurGAN and SRN improves the tracking performance of all trackers significantly."

    This 'selective deblurring' variant is an oracle: on every frame it uses ground-truth localization errors to choose the better of the raw and deblurred candidate boxes. By construction the saved output is at least as accurate as the better candidate, so the reported improvement is forced by the selection rule, not by any property of the deblurring methods. The paper then uses this observation to motivate the proposed scheme ('According to observations in Section 4.1 and 4.2, selective deblurring should help improve tracking performance'), so that motivational claim inherits the tautology. The final GAN-assessor version in Section 5 is evaluated without test-time ground truth and on held-out scenes, so the central contribution does not reduce to this oracle step.

full rationale

The core benchmark findings are empirical measurements on the constructed BVT dataset using 23 external trackers; no fitted equation is renamed as a prediction, and no load-bearing self-citation or imported uniqueness theorem appears. The only by-construction step is the Section 4.2 oracle experiment, which selects the better of two boxes using ground truth and hence guarantees the reported improvement; this is a genuine tautological step, but it is not the final contribution's validation. The proposed DeblurGAN-D assessor is fine-tuned on 20 BVT scenes and evaluated on the remaining 80, giving the central '6 trackers improved' claim independent (though same-domain) support. The Section 3.1 choice to define blurred-frame ground truth as the average of per-frame boxes is a potential external-validity artifact for moving targets, since the true smear is the union of the boxes; however, that is a benchmark-validity concern rather than a derivation that reduces to its own input, so under the stated circularity rules it does not add to the score. Overall circularity is modest and localized.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper's central claims rest on the synthetic blur generation process, the annotation-averaging ground truth rule, and the monotonicity of the fine-tuned discriminator. These are domain assumptions, not formal axioms, and none are verified against real blurred videos or independent annotation.

free parameters (3)
  • Blur level L values = 1, 2, 4, 8, 16
    Blur levels chosen by hand for the benchmark; all robustness curves and conclusions depend on this discretization.
  • Temporal sampling stride = 8 frames
    Hand-chosen stride converts high-frame-rate blurred sequences to 30fps; this affects apparent motion and tracker performance.
  • DeblurGAN-D threshold theta = 2.5
    Hand-set for all trackers with no sensitivity analysis; it determines when frames are deblurred in the proposed scheme.
assumptions (5)
  • domain assumption Temporal averaging of L consecutive 240 fps frames produces realistic motion blur equivalent to real camera and object motion blur.
    Section 3.1 uses this to generate all blurred videos; if synthetic averaging diverges from real blur, the benchmark's conclusions do not generalize to real blurred footage.
  • domain assumption Ground truth bounding boxes for blurred frames can be obtained by averaging the annotations of the medium frames in the exposure window.
    Section 3.1 defines ground truth this way; motion during the window can make the average box an inaccurate representation of the blurred target extent.
  • domain assumption The fine-tuned DeblurGAN discriminator difference D(hat I) - D(I) monotonically orders blur level and is comparable across scenes.
    Section 5.1-5.2; Eq. (4) and the practical selector depend on this monotonicity, supported only by example visualizations and no quantitative calibration.
  • domain assumption The NfS dataset's official annotations are correct and sufficient to serve as ground truth for all generated blur levels.
    The benchmark inherits NfS annotations; any annotation noise propagates into every AUC and NRS measurement.
  • domain assumption Tracker object likelihood scores computed on raw and deblurred inputs are directly comparable, so choosing the box with the larger likelihood is valid.
    Section 5.3; if likelihood scales differ across input domains, the fallback selection rule in the proposed scheme can select an inferior box.

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Cite this review

Pith. "Pith review of Effects of Blur and Deblurring to Visual Object Tracking." pith.science (2026). https://pith.science/paper/LNRVRXYJ

@misc{pith2026190807904,
  author       = {Pith},
  title        = {Pith review of: Effects of Blur and Deblurring to Visual Object Tracking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LNRVRXYJ}},
  note         = {Machine review of arXiv:1908.07904}
}
read the original abstract

Intuitively, motion blur may hurt the performance of visual object tracking. However, we lack quantitative evaluation of tracker robustness to different levels of motion blur. Meanwhile, while image deblurring methods can produce visually clearer videos for pleasing human eyes, it is unknown whether visual object tracking can benefit from image deblurring or not. In this paper, we address these two problems by constructing a Blurred Video Tracking benchmark, which contains a variety of videos with different levels of motion blurs, as well as ground truth tracking results for evaluating trackers. We extensively evaluate 23 trackers on this benchmark and observe several new interesting results. Specifically, we find that light blur may improve the performance of many trackers, but heavy blur always hurts the tracking performance. We also find that image deblurring may help to improve tracking performance on heavily blurred videos but hurt the performance on lightly blurred videos. According to these observations, we propose a new GAN based scheme to improve the tracker robustness to motion blurs. In this scheme, a finetuned discriminator is used as an adaptive assessor to selectively deblur frames during the tracking process. We use this scheme to successfully improve the accuracy and robustness of 6 trackers.

Figures

Figures reproduced from arXiv: 1908.07904 by the authors.

Figure 1
Figure 1. Results of ECO [6] and Staple CA [37, 3] on sharp, blurred or deblurred videos captured from one scene. The first subfigure shows that ECO locates the target accurately on sharp frames while losing it on blurred ones. In contrast, Staple CA can capture the ball in both cases. Such situation is not considered by existing benchmarks, e.g. OTB [57, 58] in which ECO has much higher accuracy than Staple CA. The bottom su… view at source ↗
Figure 2
Figure 2. Examples of frames that are blurred in 5 levels. ‘ [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Evaluation results of 23 trackers on the BVT benchmark. The left subfigure shows the blur robustness plot of each tracker. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Normalized robustness score (NRS) and average AUC [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: AUC gains of blurred subsets, i.e [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Evaluation results of 7 typical trackers and their four variants. DeblurGAN [ [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: AUC gains of selective deblurring based trackers, i.e. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 9
Figure 9. Figure 9: Comparing the fine-tuned discriminator with the origi [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Comparing proposed blur-robust trackers (‘* [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.