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REVIEW 4 major objections 6 minor 60 references

PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Pseudo-depth cues added to a Kalman filter resolve occlusion ambiguities in multi-object tracking, lifting DanceTrack HOTA from 54.6 to 58.2.

desk verdict Solid incremental MOT work with honest benchmark gains, but the pseudo-depth is a transform of the box bottom and the paper oversells the novelty of the depth cue. read the letter →

arxiv 2501.11288 v1 pith:TB23WGDR submitted 2025-01-20 cs.CV

Yanchao Wang , Dawei Zhang , Run Li , Zhonglong Zheng , Minglu Li This is my paper · ORCID
classification cs.CV
keywords multi-objecttrackingtracking-by-detectionpseudo-depthKalmanfilterdataassociationocclusionrobustnessdepthvolumeIoUquantizedmeasurement
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

This paper tries to show that a cheap depth proxy, read off from each 2D bounding box, can resolve the ambiguous associations that break tracking-by-detection under occlusion. PD-SORT adds pseudo-depth and its velocity to the Kalman filter state, replaces ordinary 2D IoU with a depth-volume IoU, and adds a quantized pseudo-depth cost to the matching step. On DanceTrack, where targets look alike, move nonlinearly, and constantly occlude one another, PD-SORT raises HOTA from 54.6 to 58.2 relative to its OC-SORT baseline; gains on MOT17 and MOT20 are smaller but consistent. If the claim holds, occlusion-prone tracking can be improved without depth sensors, appearance models, or slower pipelines, using only geometry already present in the 2D boxes.

What carries the argument

The load-bearing mechanism is the complementary-view pseudo-depth, $pd = 2 \times \mathrm{IMG}_h - Y_b$, which maps a box's bottom-row coordinate to a distance-like quantity that never goes negative at image boundaries. This single number is reused in three places: it is appended to the Kalman state along with its velocity $v_{pd}$; it becomes the third dimension in DVIoU, turning 2D area overlap into volume overlap; and it is binned into intervals by QPDM, so association compares coarse depth order rather than potentially noisy exact values. The same pseudo-depth also makes the recovery stage more selective, because DVIoU between a lost tracklet's last observation and a candidate detection now requires depth agreement as well as spatial overlap.

What would settle it

Run PD-SORT against its baseline on a scene where the camera is at ground level looking up at objects on a balcony or stairs, so that box-bottom position no longer tracks true distance; if HOTA and AssA gains disappear or reverse, the reported improvement is coming from the flat-ground pseudo-depth cue.

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Extended reading notes

Core claim

The central claim is that a purely geometric depth proxy, computed from each 2D bounding box, can carry much of the association burden in tracking-by-detection when appearance is uninformative and motion is non-linear. PD-SORT takes the pseudo-depth $pd = 2 \times \mathrm{IMG}_h - Y_b$ (distance from the box bottom to the bottom of an imaginary complementary view) and treats it as a real motion state: the Kalman state grows from $[x_c, y_c, s, r, v_x, v_y, v_s]$ to $[x_c, y_c, pd, s, r, v_x, v_y, v_{pd}, v_s]$. It then replaces 2D IoU with depth-volume IoU (DVIoU) in both the regular association and the lost-tracklet recovery, and adds a quantized pseudo-depth measurement (QPDM) that compares which of eight depth intervals each tracklet and detection falls into rather than comparing raw depth values. On DanceTrack test the result is HOTA 58.2 versus 54.6 for the OC-SORT baseline, with more modest gains on MOT17 and MOT20; the paper attributes the margin specifically to the depth cue disambiguating boxes that overlap heavily in 2D but sit at different depths.

Load-bearing premise

The method depends on the camera being above a flat ground plane with all tracked objects standing on it, so that the bottom edge of each bounding box honestly encodes distance; if that geometry fails, the pseudo-depth cue becomes misleading.

Editorial extensions

If this is right

  • Motion-only trackers inherit an occlusion-resolving cue without adding detectors, ReID models, or depth sensors.
  • Because the changes are confined to the Kalman state and the cost matrix, other SORT-family trackers should be able to adopt the same pseudo-depth extension directly.
  • The DanceTrack margin (+3.6 HOTA) indicates that in uniform-appearance, nonlinear-motion scenes, much of the association error was resolvable by depth order alone.
  • The modest MOT17 and MOT20 gains suggest the main benefit appears where 2D overlap is ambiguous, while simpler scenes gain only slightly.

Reading between the lines

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

  • My inference: because QPDM compares only coarse depth intervals, the active ingredient may be relative depth order rather than absolute depth; the authors do not test this separation.
  • My inference: the flat-ground assumption means the method's advantage should shrink on drone or elevated-camera footage; a direct benchmark there would define the method's operating envelope.
  • My inference: replacing the geometric pseudo-depth with a learned monocular depth estimate would test whether depth quality is the ceiling; if gains grow, the paper's cue is a floor, not a limit.
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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 / 6 minor

Summary. The paper proposes PD-SORT, an online, real-time, motion-based multi-object tracker built on OC-SORT. The authors extend the Kalman filter state with a pseudo-depth value defined as an affine function of the bounding box bottom row, introduce a Depth Volume IoU (DVIoU) that reweights 2D IoU by this pseudo-depth, and add a Quantized Pseudo-Depth Measurement (QPDM) cost based on min-max normalized, interval-quantized pseudo-depth values. Camera motion compensation is also integrated. The method is evaluated on DanceTrack, MOT17, and MOT20 under the private-detection protocol, reporting consistent improvements over OC-SORT, most notably +3.6 HOTA on DanceTrack, alongside component ablations on the DanceTrack validation set.

Significance. If the central claim is taken at face value, the paper would provide a simple, real-time, appearance-free tracker that improves occlusion robustness by integrating a depth-like cue into the motion model. The empirical work is substantial: benchmark results are from official MOT servers, the baseline and proposed method share the same detections, and the ablations show monotone gains from each added module. The paper also ships code and reports runtime. However, the significance is substantially qualified by the fact that the pseudo-depth is a deterministic affine transform of the bounding box bottom row, which is already implied by the existing Kalman state. This makes the 'depth' interpretation, and the claim that depth supplies new discriminative information, not supported by the current presentation. The observed gains may be real and reproducible, but they are better described as gains from an engineered bottom-row consistency feature rather than from a genuinely independent depth measurement.

major comments (4)
  1. [Section III-A, Eq. (1) and Eq. (3)] The pseudo-depth defined as pd = 2*IMGh - Yb is an affine, monotonically decreasing function of the detection's bottom row Yb. Since Yb is fully determined by the existing Kalman state (xc, yc, s, r) through the box relation (Yb = yc + 0.5*sqrt(s/r)), the augmented state X = [xc, yc, pd, s, r, vx, vy, vpd, vs] treats a derived coordinate as an independent state. The Kalman filter's diagonal process and measurement noise covariances therefore encode a constraint that does not match the data-generating process; the augmented state is internally redundant. The paper should either provide a principled justification for why the derived coordinate should be modeled as an independent state with its own velocity, or reframe the contribution as using bottom-row consistency within the existing state space.
  2. [Section III-C, Eq. (7) and Algorithm 1] The min-max normalization in QPDM is invariant to affine monotone transformations of the input. Because pd = C - Yb with constant C = 2*IMGh, normalizing pd yields (Yb_max - Yb)/(Yb_max - Yb_min), which is exactly the reversed normalized bottom row. Consequently, QPDM is indistinguishable from quantized bottom-row consistency; it does not use depth information beyond what is already present in the 2D box. The ablation in Table V demonstrates that quantization helps, but it does not support the claim that the pseudo-depth interpretation provides extra discriminative information. The authors should add a comparison where the same QPDM pipeline is applied directly to Yb, or to a genuinely independent depth estimate, to isolate the source of the gains.
  3. [Section III-B, Eq. (4)-(6)] DVIoU is not a three-dimensional volume IoU in any geometric sense; the 'depth volume' is the 2D intersection area multiplied by a scalar that is an affine function of the box bottom row (pd = C - Yb). Thus DVIoU is a reweighting of the standard 2D IoU by a Yb-dependent factor, not an intersection over union computed in a space with an independent depth axis. The improvement over standard IoU in Table VI may be a legitimate empirical effect, but the 'depth volume' terminology is misleading. The authors should either rename DVIoU to reflect its actual form or provide evidence that the geometric interpretation as a 3D volume is consequential, for example by comparing against an equivalent reweighting based directly on Yb.
  4. [Abstract and Section I] The central claim that pseudo-depth 'significantly alleviates the occlusion-induced ambiguous associations' implies that depth is a new cue. Given that the pseudo-depth is a deterministic transform of an existing 2D box coordinate, the benchmark improvements on DanceTrack do not by themselves demonstrate that a depth cue is responsible. A concrete test would be to rerun PD-SORT with the pseudo-depth everywhere replaced by Yb (or by an independent monocular depth estimate) and compare the metrics. Without such an experiment, the novelty claim that this is the first work to incorporate depth as a state in a pure motion-based 2D MOT is overstated.
minor comments (6)
  1. [Section I, last paragraph of Introduction] The phrase 'We inherent the observation-centric idea' should be 'We inherit'; the same typo appears in Section III-E.
  2. [Eq. (4)] The formula for winter contains a typo: 'max(x1_1 - x2_1)' should be 'max(x1_1, x2_1)' (the max of the two left coordinates), otherwise the expression is dimensionally inconsistent.
  3. [Algorithm 1, lines 7 and 15] In the QPDM pseudocode, 'minprevious ← 1' inside the loop should be 'minprevious ← mincurrent' (or the equivalent) to advance the interval lower bound; as written, all intervals are tested against [1, mincurrent] repeatedly, which does not match the interval subdivision described in the text.
  4. [Section IV-C-1, first paragraph] The text reads 'the pesudo-depth' – spelling should be 'pseudo-depth'.
  5. [Section IV-B-2, computational efficiency] The sentence 'we test the frames per second (FPS) of our method (28.7 FPS) and the baseline (35.1 FPS) on on the same device' contains a duplicated 'on' and should be reworded.
  6. [Section III-C, heading and text] The abbreviation for Depth Volume IoU is inconsistently written as 'DvIoU' in the section heading and 'DVIoU' elsewhere; the notation should be unified.

Circularity Check

1 steps flagged · score 2.0 of 10

Mild attribution circularity: 'pseudo-depth' is, by the paper's own Eq. 1, an affine transform of the box-bottom row, so the depth cue adds no new measurement; the headline benchmark results are externally evaluated and not forced.

  1. renaming known result [Section III-A, Eq. 1; claims in Section IV-B2 and Fig. 2 caption]
    "For the definition of pseudo-depth, as in SparseTrack [15], we first used the projection of depth ... our pseudo-depth pd is computed as in Eq. 1. pd = 2 × IMGh − Yb (1) ... Nevertheless, we use pseudo-depth to provide additional cues for association."

    By the paper's own Eq. 1, pd is a deterministic affine (monotone decreasing) function of the detection's bottom row Yb, which is itself fixed by the 2D bounding box already in the SORT measurement stream (Yb = yc + h/2, with h determined by s and r of Eq. 2). The KF state in Eq. 3 therefore appends no new measurement to the observation stream. QPDM's frame-wise min-max normalization (Eq. 7) is invariant under affine monotone transforms of pd, making the C_QPD cost (Eq. 8) equivalent to quantized bottom-row consistency; DVIoU (Eqs. 4–6) merely reweights 2D IoU by a monotone function of Yb.

full rationale

PD-SORT's headline results (DanceTrack HOTA 58.2 vs OC-SORT 54.6; MOT17 +0.8 HOTA; MOT20 +0.5 HOTA) are measured on official test servers under the private-detection protocol with detections shared with the baseline. No self-citation is load-bearing: all references are external, SparseTrack's projection-based pseudo-depth is properly attributed, and OC-SORT, BoT-SORT, and ByteTrack are third-party baselines. No fitted parameter is renamed as a prediction: the QPDM weight, OCM weight, and interval count are validation-set hyperparameters reported on held-out test sets, which is standard practice. The one reductive element is definitional: Eq. 1 makes pd = 2*IMGh − Yb, a deterministic affine transform of the box-bottom row, so the KF's appended 'depth' state (Eq. 3) carries no information beyond (yc, s, r); QPDM's frame-wise min-max normalization (Eq. 7) is invariant to that affine transform, making the C_QPD cost (Eq. 8) equivalent to quantized bottom-row consistency, and DVIoU (Eqs. 4–6) reweights 2D IoU by a monotone function of Yb. Sections IV-B2 and Fig. 2 attribute the gains to depth supplying 'additional cues' and compensating 'when 2D information fails,' which the paper's own equations show is the same 2D box-bottom signal renamed. This is a novelty/attribution overstatement rather than a circular derivation of the results, since the numbers themselves are not forced by construction; hence a low score.

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

The method introduces no invented entities. The main burden is the treatment of a deterministic function of existing 2D box states as new independent Kalman filter states, plus the geometric flat-ground assumption behind pseudo-depth.

free parameters (4)
  • lambda_1 (QPDM weight) = 0.2 (DanceTrack, MOT17), 0.36 (MOT20)
    Weight of the quantized pseudo-depth cost in Eq. 11; tuned per dataset in Section IV-A.
  • lambda_2 (OCM weight) = 0.2 (DanceTrack, MOT17), 0.04 (MOT20)
    Weight of the velocity-direction consistency cost inherited from OC-SORT; tuned per dataset.
  • interval_num (QPDM sub-intervals) = 8
    Selected by ablation on the DanceTrack validation set (Table V), then used for all benchmarks.
  • IoU association threshold = 0.3 (DanceTrack, MOT17), 0.35 (MOT20)
    Association threshold in the Hungarian matching; differs per dataset.
assumptions (4)
  • domain assumption Pseudo-depth validity: the camera is above the ground plane and all objects are on the same plane.
    Stated in Section III-A: 'Such projection-based pseudo-depth estimation relies on the assumptions that the image capture device is above the ground plane and all objects in the scene are on the same plane.'
  • domain assumption Relative depth of an object with respect to others fluctuates only in narrow intervals across consecutive frames.
    Motivation for QPDM in Section III-C: 'in successive frames, the relative depth of the object with respect to other objects fluctuate only in narrow intervals.'
  • domain assumption The constant-velocity Kalman filter process model is adequate for the pseudo-depth and its velocity.
    The state transition treats pd and vpd as independent linear states (Eq. 3), even though pd is a deterministic function of yc, s, and r; this is a modeling approximation.
  • domain assumption Detector outputs are consistent and reliable.
    The entire tracker depends on YOLOX detections; the paper does not analyze detector failure modes or their effect on pseudo-depth.

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Cite this review

Pith. "Pith review of PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues." pith.science (2026). https://pith.science/paper/TB23WGDR

@misc{pith2026250111288,
  author       = {Pith},
  title        = {Pith review of: PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TB23WGDR}},
  note         = {Machine review of arXiv:2501.11288}
}
read the original abstract

Multi-object tracking (MOT) is a rising topic in video processing technologies and has important application value in consumer electronics. Currently, tracking-by-detection (TBD) is the dominant paradigm for MOT, which performs target detection and association frame by frame. However, the association performance of TBD methods degrades in complex scenes with heavy occlusions, which hinders the application of such methods in real-world scenarios.To this end, we incorporate pseudo-depth cues to enhance the association performance and propose Pseudo-Depth SORT (PD-SORT). First, we extend the Kalman filter state vector with pseudo-depth states. Second, we introduce a novel depth volume IoU (DVIoU) by combining the conventional 2D IoU with pseudo-depth. Furthermore, we develop a quantized pseudo-depth measurement (QPDM) strategy for more robust data association. Besides, we also integrate camera motion compensation (CMC) to handle dynamic camera situations. With the above designs, PD-SORT significantly alleviates the occlusion-induced ambiguous associations and achieves leading performances on DanceTrack, MOT17, and MOT20. Note that the improvement is especially obvious on DanceTrack, where objects show complex motions, similar appearances, and frequent occlusions. The code is available at https://github.com/Wangyc2000/PD_SORT.

Figures

Figures reproduced from arXiv: 2501.11288 by the authors.

Figure 1
Figure 1. Three examples of occlusion-induced tracking failures. The samples are OC-SORT results on DanceTrack, where objects have diverse motions and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A comparison of association without depth information and with depth information on DanceTrack [14]. Bounding boxes and dashed arrows of [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Pipeline of PD-SORT. The preparation stage estimates pseudo-depth for new detections and uses CMC to correct both motion states from KF and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Illustration of IoU and DVIoU. By integrating pseudo-depth (the extra [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Radar chart of the gains obtained through different combinations [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Visualization of the tracking results between the 2D tracker OC-SORT and the proposed PD-SORT tracker utilizing pseudo-depth on the DanceTrack [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.