{"id":"6b2c4e80-a217-4abc-be8b-9c9df05474d3","arxiv_id":"2507.03016","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":5,"one_line_summary":"A homography-based monocular system estimates stride length from pose-detected foot contacts, validated only by cross-video consistency for three runners.","lead":"This paper presents a camera-only pipeline that estimates a runner's stride length by detecting track lines with classical image processing and foot landings with a pose-estimation model. It reports stride values for three athletes across two videos, but without ground-truth measurements the estimates cannot be validated.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The metric scale of every reported stride length is set by an unstated homography world-plane calibration; without the real-world coordinates of the track intersections in §2.4, cross-video consistency cannot validate absolute stride lengths.","rationale":"The reader's weakest_assumption identifies the same load-bearing concern: the homography's metric scale comes from track-line intersections whose world coordinates are never stated. My reading of the full text confirms this is the single most consequential gap. The pipeline is internally coherent in its image-processing steps, but the output 'meters' are only as meaningful as the unstated world-plane calibration. Cross-video agreement, which is the only quantitative evidence offered, is invariant to a global scale error and therefore cannot support the abstract's 'estimate stride length' claim or the conclusion's 'reliability' claim. Additional issues—no ground truth, no code/data, the unfinished editorial note in §2.3.2, and the missing Algorithm reference—compound the problem, but the scale calibration is the load-bearing point. Because the reader already rejected the paper on this basis, my stress-test does not move the verdict.","tokens_in":5629,"tokens_out":4949,"duration_ms":57395,"concrete_test":"Obtain from the authors the exact metric coordinates assigned to the four track intersections used in §2.4, and verify them against the physical track (e.g., standard lane width of 1.22 m and known spacings of the horizontal markings). Then re-run the pipeline with those world coordinates perturbed by ±2% and compare the resulting average stride lengths. If the stride values shift by more than the 0.04–0.10 m differences in Table 1, the reported cross-video consistency cannot validate the absolute metric claim; if they shift by less, the scale ambiguity is not decisive.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—metric stride length from a single monocular camera—depends entirely on the world-plane coordinates used to build the homography. In §2.4, H is defined by point correspondences between the image plane and 'metric coordinates' in R^2, but the manuscript never states the actual measured distances or coordinates assigned to the four track intersections (derived from the fourth/fifth horizontal lines with vertical lines in §2.3.1) that feed H. The Hough and vanishing-point stages only supply pixel coordinates; the metric factor must come from an external assumption about track geometry. If that assumed spacing is off by a factor k, every projected foot position and every Euclidean stride distance in §3 scales by k. The reported cross-video consistency (differences of 0.04–0.10 m in Table 1) is a relative check under the same camera/track setup; a wrong scale would leave these differences unchanged and therefore provides no evidence for the 'reliability' claimed in §4. The paper itself acknowledges that no ground truth is available ('Since there is no ground truth ... we can only compare consistency'), so the absolute values 1.66–1.90 m are not anchored to anything described in the manuscript. This is the weakest link in the evidence chain: without the missing world coordinates, the method is not reproducible and the metric claim is unverified.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a markerless pipeline for estimating stride length from monocular video of runners on an athletics track. The method combines Canny edge detection, probabilistic Hough transforms, vanishing-point estimation, and a planar homography to map image points to metric coordinates, then uses EfficientPose keypoints with a stationarity heuristic to identify foot contacts and computes Euclidean distances in the transformed plane. Experiments on three athletes, with two videos each, report average stride lengths of 1.66–1.90 m with cross-video differences of 0.04–0.10 m; no ground truth is available. The conclusion states that the results confirm the method's reliability.","tokens_in":5954,"tokens_out":5213,"duration_ms":62433,"significance":"If validated, the approach would provide a low-cost, markerless tool for stride monitoring in athletics, and the combination of classical geometric line detection with a neural pose estimator is a reasonable engineering choice. The paper is transparent about the absence of ground truth and lists validation as future work, which is a strength. However, the current evidence supports only consistency under a fixed camera/track setup, not accuracy or reliability in metric units; the unstated world-plane calibration is a critical gap. The contribution is therefore at the feasibility-demonstration level rather than a validated measurement method.","major_comments":[{"comment":"The homography in Section 2.4 is computed from point correspondences between pixel coordinates and “metric coordinates” in R^2, but the manuscript nowhere states the actual real-world coordinates or measured distances assigned to the four track intersections (derived from the fourth and fifth horizontal lines in Section 2.3.1). Since H defines the absolute scale of every stride length, an error of factor k in the assumed track geometry multiplies all reported distances by k. The cross-video consistency in Table 1 is invariant under this global scaling, so it cannot validate the absolute values 1.66–1.90 m. Please specify the measured world coordinates and how they were obtained; otherwise the method is not reproducible and the metric claim is unverified.","section":"Section 2.4, Eq. (1)"},{"comment":"The only quantitative evidence is consistency of average stride lengths between two videos for three athletes, with differences of 0.04–0.10 m. The text itself states “Since there is no ground truth … we can only compare the consistency between the two videos,” and the statement that “the foot position is ever correctly detected for each steps” (presumably “never correctly detected for every step”) indicates unquantified detection errors. Without ground-truth measurements or any error bar, consistency does not establish accuracy; it is fully compatible with a constant systematic error. The concluding sentence of Section 4 (“The results obtained confirm the reliability of the method”) is therefore not supported by the reported experiments. Add a ground-truth comparison (e.g., motion capture, manual stride marking, or known track distances) or substantially weaken the claim to feasibility.","section":"Section 3, Table 1"},{"comment":"The rule that a toe keypoint remaining at the same pixel for two consecutive frames is a foot strike is asserted without tolerance, validation, or sensitivity analysis. Pose-estimation jitter, the 30 fps frame rate, and camera perspective all affect this test, and mis-detected contacts directly change the Euclidean distances in the transformed plane. Please report the distribution of detected contact points, compare against manual annotations, or analyze sensitivity to the stationarity threshold.","section":"Section 2.5"},{"comment":"The paper states that “the median of the matrices computed for each individual frame was taken” to obtain the final homography, but it does not define the median operation. Homography matrices form a projective group rather than a vector space, so an element-wise median is not geometrically meaningful and may yield a matrix that does not represent a valid plane transformation. Please define the aggregation procedure and show that the reported stride lengths are stable under alternative choices.","section":"Section 3 (homography aggregation)"}],"minor_comments":[{"comment":"The sentence “Notice that if ‘inclinate’ was intended to mean ‘near-vertical’ …” is an unedited editorial or translation note and should be removed from the final text.","section":"Section 2.3.2"},{"comment":"The reference to “Algorithm ??” is incomplete; either provide the pseudocode or remove the reference.","section":"Section 2.3.1"},{"comment":"The affiliation contains a typo: “sIstemi” should be “Sistemi”.","section":"Affiliation"},{"comment":"The phrase “the foot position is ever correctly detected for each steps” is grammatically incorrect and ambiguous; it should be revised to “the foot position is not always correctly detected for every step”.","section":"Section 3"},{"comment":"Figure 12 is described as showing results for two runners, while Table 1 reports three athletes; clarify whether the third athlete's result is omitted from the figure.","section":"Figure 12 and Table 1"}],"recommendation":"reject","confidential_remarks":"The manuscript is a feasibility study with a plausible pipeline but no quantitative validation. The missing world-coordinate calibration is a fixable reporting gap, but the reliability claim cannot be substantiated without ground-truth measurements, which the paper itself defers to future work. I recommend rejection; a substantially revised version with a proper validation study and explicit calibration details could be reconsidered."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"This is a feasibility study that assembles Canny edge detection, a probabilistic Hough transform, homography, and EfficientPose to estimate stride length from a single monocular camera. The one genuinely useful idea is the stationarity-based foot-contact detection: a keypoint that stays in the same pixel for two consecutive frames marks a landing. That is simple, plausible, and worth testing.\n\nThe paper is honest about the lack of ground truth in Section 3, and it correctly cites Cronin et al. for prior markerless athletics pose analysis. The pipeline is described clearly enough at a high level, and the cross-video consistency for three runners is a reasonable sanity check. Those are real merits.\n\nThe load-bearing problem is the homography calibration. Section 2.4 defines H as mapping image points to “metric coordinates” but never gives the actual real-world coordinates assigned to the four track intersections. Without those, every stride length is scaled by an unknown factor. The stress-test note is right: the consistency check cannot uncover a wrong scale because it only compares relative values under the same camera setup. So the absolute values 1.66–1.90 m are not anchored to anything described in the manuscript. The conclusion’s claim that the results “confirm the reliability of the method” goes beyond the evidence.\n\nThere are also clear signs of an unfinished manuscript: an editorial note left in Section 2.3.2, a missing algorithm reference (“Algorithm ??”), and the typo “the foot position is ever correctly detected,” which presumably should read “never.” No code or data is provided, so the method is not reproducible as submitted.\n\nNone of this makes the idea worthless. The stationarity heuristic is testable, and if the world coordinates were supplied, the same pipeline could be validated against ground truth in a future study. But as it stands, the central metric claim is unverified. A serious referee could push the authors to provide the calibration and a ground-truth comparison, so I would not desk-reject it outright, but it should not be accepted in anything close to this form. I would not cite it in my own work, and I would not bring it to reading group.","headline":"A practical-looking stride-length pipeline undone by an unstated homography scale; the consistency check is real but relative, so the absolute metric claim is unverified.","tokens_in":6443,"tokens_out":1728,"would_cite":false,"duration_ms":19487,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A homography built from track-line intersections and pose-detected toe points yields markerless stride-length estimates from monocular video.","keywords":["pose estimation","stride length estimation","homography","monocular vision","track line detection","markerless motion analysis","athletics video analytics","probabilistic hough transform"],"falsifier":"Film the same athlete on a track while a motion-capture system or force plates record true stride length; if the homography-based estimates differ from the ground truth by more than the reported run-to-run variability, the metric scale or the contact detection is wrong. A simpler check is to place two markers a known distance apart on the track inside the camera view and compare the homography-reconstructed distance to that known value.","tokens_in":5442,"feed_emoji":"🏃","tokens_out":5519,"duration_ms":58474,"temperature":0.7,"pith_summary":"The paper aims to show that a runner's stride length can be measured from ordinary monocular video without markers, using pose estimation to find the feet and a homography built from track-line intersections to convert image positions into metric distances. The authors combine edge detection and a probabilistic Hough transform to find the track's horizontal and vertical lines, use their intersections to define a homography, and then project toe keypoints (detected by a pose model and refined by a stationarity rule) into a top-down world plane. Across three athletes, each filmed in two separate videos, the average stride lengths are stable enough (about 1.66–1.90 m, with 2–6 percent variation between videos) that the authors argue the tool is usable for coaching and training. If true, this gives coaching staff a low-cost, unobtrusive way to monitor gait parameters like stride length and pace transition in practice and competition.","feed_headline":"Stride length emerges from a single camera and track-line geometry","feed_subtitle":"No markers, no wearables: toe points and track-line geometry give consistent per-athlete stride estimates across videos.","key_machinery":"The load-bearing mechanism is the planar homography matrix $H \\in \\mathbb{R}^{3\\times 3}$ that maps image-plane pixel coordinates to world-plane metric coordinates using at least four point correspondences. Here the correspondences come from intersections between horizontal and vertical track lines detected by a probabilistic Hough transform, with the homography recomputed per frame and the median matrix used to suppress outliers. A second component is the contact-point detector: a toe keypoint from the EfficientPose pose model is treated as a foot landing when it stays in the same pixel for two consecutive frames, and each detected point is projected along the line through the vanishing point before being mapped by the homography.","core_discovery":"The central claim is that a planar homography estimated from the intersections of detected track lines turns a monocular camera view into a metric top-down view, and that Euclidean distances between consecutive foot-landing points in that view are stride lengths. The authors report that the method yields reproducible per-athlete estimates across two videos—1.66 vs 1.76 m for one runner, 1.87 vs 1.80 m for a second, and 1.90 vs 1.86 m for a third—and take this consistency, in the absence of ground truth, as evidence of reliability. The intended conclusion is that markerless video analysis can support training and competition monitoring of athletes.","pith_inferences":["The method's absolute accuracy is untested: without ground truth, a constant scale error from wrong assumed line positions would hide in the cross-video consistency; a validation against motion capture or known-distance markers would settle the metric scale.","The two-frame stationarity rule fixes an implicit temporal resolution; at 30 fps, very brief ground contacts in sprinting may be missed, so high-frame-rate capture is a natural stress test.","Because the homography is derived from track lines in each frame, camera motion or zoom that keeps lines visible could in principle be tolerated, extending the method beyond fixed cameras.","The paper's future step of adapting this to non-track settings is the logical next move: replace the line-based homography with SLAM or depth information, and the rest of the pose-to-stride pipeline carries over."],"forward_implications":["Coaches can obtain per-athlete stride-length curves from a single fixed camera, without markers or wearables, as long as track lines are visible.","Because the metric scale sits in the homography, the same pipeline works for any track whose line spacing is known; no per-athlete calibration is required.","The combination of classical line detection with a real-time pose model points to a practical pipeline that can run near video frame rate.","If stride length is stable across videos for a given athlete, then changes in the estimate can flag fatigue, pacing adjustments, or speed changes for a coach."],"supporting_citations":[{"why":"Supplies the EfficientPose model used to detect the toe keypoints that define foot landings.","marker":"[9]"},{"why":"OpenPose is the alternative pose estimator the authors compare against and reject for speed, motivating the choice of EfficientPose.","marker":"[10]"},{"why":"MoveNet is the other alternative pose estimator considered, rejected for lack of 3D support; the comparison informs the method choice.","marker":"[11]"},{"why":"Demonstrates prior feasibility of markerless OpenPose motion analysis in a real athletics competition, the context this work extends.","marker":"[6]"}],"fun_headline_variants":["Monocular video yields stride length via track-line homography","Pose plus track-line intersections give stride length on video","Single-camera stride estimates from pose and line geometry","No wearables: track-line homography computes stride length","Track-line homography maps one camera view to metric stride"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The method assumes the real-world spacing and position of the track lines used to build the homography are known and correct, because that is the only source of metric scale; if the assumed line geometry is wrong, every stride estimate is scaled by the same wrong factor and the cross-video consistency cannot reveal the error.","fun_headline_variants_meta":{"raw":{"variants":["Monocular video yields stride length via track-line homography","Pose plus track-line intersections give stride length on video","Single-camera stride estimates from pose and line geometry","No wearables: track-line homography computes stride length","Track-line homography maps one camera view to metric stride"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001228,"raw_usage":{"total_tokens":4990,"prompt_tokens":830,"completion_tokens":4160,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":446,"completion_tokens_details":{"reasoning_tokens":4080}},"tokens_in":446,"tokens_out":4160,"duration_ms":31128,"temperature":1.0,"reasoning_tokens":4080,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T20:44:12.632273+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Film the same athlete on a track while a motion-capture system or force plates record true stride length; if the homography-based estimates differ from the ground truth by more than the reported run-to-run variability, the metric scale or the contact detection is wrong. A simpler check is to place two markers a known distance apart on the track inside the camera view and compare the homography-reconstructed distance to that known value.","supporting_citations":[{"cited_title":"Efficientpose: Efficient human pose estimation with neural architecture search","cited_arxiv_id":null,"evidence_quote":"Supplies the EfficientPose model used to detect the toe keypoints that define foot landings."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"OpenPose is the alternative pose estimator the authors compare against and reject for speed, motivating the choice of EfficientPose."},{"cited_title":"Movenet: A deep neural network for joint profile prediction across variable walking speeds and slopes","cited_arxiv_id":null,"evidence_quote":"MoveNet is the other alternative pose estimator considered, rejected for lack of 3D support; the comparison informs the method choice."},{"cited_title":"Feasibility of openpose markerless motion analysis in a real athletics competition","cited_arxiv_id":null,"evidence_quote":"Demonstrates prior feasibility of markerless OpenPose motion analysis in a real athletics competition, the context this work extends."}],"review_version":1}