Aerial multi-object tracking by detection using deep association networks

Ajit Jadhav; Brejesh Lall; Prerana Mukherjee; Vinay Kaushik

REVIEW 3 major objections 7 minor 56 references

Aerial multi-object tracking by detection using deep association networks

T0 review · 3 major / 7 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Small drone objects evade standard detectors because their anchors are too large; six denser scales plus Squeeze-and-Excitation lift VisDrone validation AP from 14.45 to 17.19 and make tracking-by-detection competitive.

desk verdict Benchmark-tuning paper whose claimed detection gain is confounded by inconsistent training schedules; the tracking result inherits the problem. read the letter →

arxiv 1909.01547 v1 pith:WJODLMHM submitted 2019-09-04 cs.CV

Ajit Jadhav , Prerana Mukherjee , Vinay Kaushik , Brejesh Lall This is my paper · ORCID

classification cs.CV

keywords aerialobjectdetectionmulti-objecttrackinganchorscalesSqueeze-and-ExcitationRetinaNetDeepSORTVisDronesmall

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 is trying to establish that aerial multi-object tracking can be made to work by fixing the detector's anchor geometry rather than by inventing a new tracking paradigm. It claims that standard RetinaNet anchors, designed for natural images, leave objects of 8–32 pixels in drone footage without any matching anchor, so those objects are invisible to training; replacing the three per-level scales with six denser scales (0.1, 0.25, 0.5, 1, $2^{1/3}$, 2.2) raises recall, and inserting Squeeze-and-Excitation blocks before the feature pyramid raises validation AP from 15.39 to 17.19. On the tracking side, it claims that a DeepSORT-style association network trained on COCO patches, with detector confidence fused into the association metric, produces tracks on VisDrone MOT that beat existing baselines on small pedestrians and cars even when the detector's test-set AP is not the best. A sympathetic reader would care because the recipe is simple and transferable: better small-object detection, not a bespoke tracker, is the bottleneck for drone video.

What carries the argument

The load-bearing objects are the modified anchor scale set and the Squeeze-and-Excitation blocks in the detector, together with the DeepSORT association metric in the tracker. Anchor scales are the reference boxes a one-stage detector compares against; the paper replaces RetinaNet's three per-level scales with six scales covering a wider and denser size range so that very small objects can be matched during training. SE blocks are channel-wise attention units that learn to reweight feature channels; inserted before the feature pyramid, they give the detector better-localized features. The tracker uses a deep association network, a CNN trained with cosine metric learning to embed object patches so that the same object is close in feature space, and fuses its similarity score with the detector's confidence when matching detections across frames.

What would settle it

Run the paper's detector on the VisDrone validation split in four configurations — original anchors, dense anchors, dense anchors plus SE, and dense anchors with SE removed — and check that AP moves roughly along the reported path (14.45 → 15.39 → 17.19); separately, replace the COCO-trained association network with one trained on VisDrone MOT patches and compare tracking AP and ID switches. If either variation does not reproduce the reported differences, the claimed mechanisms are not the cause.

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Extended reading notes

Core claim

The central discovery is that scale coverage, not network capacity, is what unlocks small-object detection in aerial images. With RetinaNet's default anchors, objects below roughly 32×32 pixels have no assigned anchor and therefore do not contribute to training; switching to the six scales 0.1, 0.25, 0.5, 1, $2^{1/3}$, 2.2 at each pyramid level assigns anchors to those objects and lifts average recall at 500 detections per image from 21.38% to 30.49%. Adding Squeeze-and-Excitation blocks between the ResNet outputs C3–C5 and the feature pyramid then improves localization more than recall, raising validation AP from 15.39 to 17.19 and the IoU=0.50 AP from 33.13 to 37.69. The tracking claim is that these detections feed a DeepSORT association pipeline whose appearance embedding is trained on COCO patches and whose distance is fused with detector confidence; on VisDrone MOT the reported result is an overall AP of 13.88 with strong per-class numbers for pedestrians (18.61) and cars (32.2), which the paper attributes to the detector producing many low-confidence but correct detections that the association network can still match.

Load-bearing premise

The tracking pipeline assumes that a deep association network trained on COCO person patches still produces embeddings that tell apart and correctly re-identify the same small objects seen from a drone's aerial viewpoint; the paper does not ablate this transfer, and since detector confidence is fused into the association metric, a failure of those embeddings would directly lower the reported tracking accuracy.

Editorial extensions

If this is right

If the central claim is right, any single-stage detector trained on dense small-object data should adopt scale sets that cover the dataset's smallest objects instead of default natural-image anchors.
Tracking quality follows from detection recall at high max detections: producing many low-confidence correct detections is more useful for association than producing few high-confidence ones.
The confidence-plus-appearance fusion rule should transfer to other tracking-by-detection systems, making the tracker more stable when the appearance embedding is weak or domain-shifted.
The reported gains imply that channel recalibration before the feature pyramid is a cheap way to improve localization in aerial detection, independent of the anchor change.
The method's strong per-class results on pedestrians and cars, paired with weaker results on buses and vans, suggest category imbalance in VisDrone, not the architecture, is the next obstacle.

Reading between the lines

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

The paper does not ablate the COCO-trained association network; a natural extension is to fine-tune it on VisDrone MOT patches and compare ID-switch rates, which would isolate how much of the tracking gain is due to appearance-embedding transfer.
Because the reported AP gain from SE blocks is mostly at higher IoU thresholds, the next testable step is pairing the dense anchors with a stronger regression head, such as iterative refinement, to push localization further.
If the COCO pretraining transfers well to aerial vehicles, the same detector-plus-association pipeline could be tried on other dense aerial benchmarks without retraining the appearance model, which would confirm whether the transfer is generic rather than VisDrone-specific.
A direct probe of the confidence-fusion rule would be to vary its weight between appearance distance and detector score; if tracking degrades smoothly as the confidence weight goes to zero, the fusion is doing real work.

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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

The short version: this is a benchmark-tuning paper that reports a nice validation gain on VisDrone for RetinaNet with denser anchor scales and SE blocks, but the gain cannot be attributed to those changes because the training schedules differ across the ablation. The tracking result is a standard DeepSORT-type pipeline with a COCO-trained association network, and it does reasonably well on pedestrians and cars, but the detection numbers that feed it are shaky.

What's genuinely here: an honest attempt to adapt a single-stage detector to drone imagery. The anchor-scale analysis makes sense—VisDrone has many objects smaller than 32x32 pixels, and the default RetinaNet anchors miss them. Adding SE blocks to the FPN inputs is a reasonable, low-cost modification. The paper also grounds the association network on COCO object patches rather than training on VisDrone itself, which is a plus for reproducibility. Tables are present, and the MOT comparison includes several baselines.

The soft spots are real and load-bearing. Section 3.5 gives three different training schedules: base RetinaNet runs 26 epochs at 1618 iters/epoch, batch 4; dense-scales model runs 25 epochs at 3246 iters/epoch, batch 4; dense-scales+SE runs 27 epochs at 3246 iters/epoch, batch 2. That's roughly 42k, 81k, and 88k iterations, with a batch-size change. Then Section 4.3 says the network is trained for 50k iterations with batch size 1—a third configuration. So the validation AP increase from 14.45 to 17.19 is not attributable to the architecture. More training steps or smaller batches can raise small-object AP by themselves, and the paper provides no error bars or multiple runs. The official test-set result in Table 3 shows Ours at 11.19 AP, below the RetinaNet baseline at 11.81 AP, which the paper acknowledges but waves off. That's a red flag for generalization. The tracking evaluation inherits the problem: if the detector improvement is not established, the MOT comparison in Table 4 is on weaker footing, and there is no ablation of the COCO-to-aerial transfer or of the confidence fusion.

Overall: the paper is readable and the direction is sensible, but the central empirical claim doesn't hold as presented. This is the kind of result a workshop might accept as a challenge report, but I wouldn't send it to a serious journal as-is. It needs a controlled ablation with fixed training budget, code or checkpoints, and at least a couple of runs. The idea isn't bad; the evidence is.

Referee Report

3 major / 7 minor

Summary. The paper proposes a detection and multi-object tracking framework for aerial imagery using the VisDrone2019 benchmark. The detection module is RetinaNet with modified anchor scales (0.1, 0.25, 0.5, 1, 2^{1/3}, 2.2) and Squeeze-and-Excitation blocks, and the tracking module adapts DeepSORT with a deep association network trained on COCO patches. The authors report validation AP gains from 14.45 to 17.19 (Tables 1-2) and competitive tracking results on VisDrone MOT (Table 4), while acknowledging sub-optimal test-set detection performance (Table 3).

Significance. If the reported gains were established under controlled conditions, the anchor-scale and SE-block modifications would be a simple, potentially transferable recipe for small-object detection in aerial images, and the confidence-fused association metric would be a modest but useful extension of DeepSORT. The paper uses a public benchmark and external COCO pretraining, which provides some grounding. However, the central detection claim is not currently supported: the validation ablation is confounded, the official test-set result contradicts the claimed improvement, and the tracking contribution lacks ablations or standard MOT metrics. The significance is therefore conditional on substantial additional experimentation.

major comments (3)

[§3.5 and §4.3, Tables 1-2] The detection ablation in Tables 1-2 is confounded by the training schedule. Section 3.5 states that the base RetinaNet was trained for 26 epochs at 1618 iterations/epoch with batch size 4 (about 42k optimization steps), the dense-scales model for 25 epochs at 3246 iterations/epoch with batch size 4 (about 81k steps), and the dense-scales+SE model for 27 epochs at 3246 iterations/epoch with batch size 2 (about 88k steps). Section 4.3 then states 'We train the network for 50K iterations with the batch size set to 1,' a third incompatible schedule. Because the number of optimization steps, batch size, and schedule vary together with the architectural modifications, the AP increase from 14.45 to 17.19 cannot be attributed to the proposed anchor scales and SE blocks; a longer schedule or smaller batch size alone can raise AP and AR for small objects. The tracking comparisons in Table 4 inherit this attribution problem.
[Table 3] On the official VisDrone test set, the full model ('Ours') achieves AP=11.19, below the RetinaNet baseline of AP=11.81 listed in the same table. This directly contradicts the abstract's claim that the modifications yield 'significant improvements in performance' and is not adequately explained by the statement in §4.4 that the detector performs 'sub-optimally' on the test set. Since the tracking pipeline consumes these detections, the positive tracking results in Table 4 do not compensate for the failure of the central detection claim on the benchmark's official evaluation split.
[§3.4, §4.3, and Table 4] The deep association network is trained on COCO person patches and then applied to all ten VisDrone categories (cars, trucks, buses, pedestrians, etc.) without any ablation measuring whether the embedding transfers across category and viewpoint. The paper states that the detector confidence is fused with the deep association metric, but it does not specify the fusion rule or its hyperparameters, and no experiment separates the contribution of the appearance embedding from the detector confidence. Without such an ablation, the claimed benefit of training the association network and the robustness claim in §4.4 are not established. Table 4 also compares only against trackers from 2011-2014 and reports no CLEAR MOT metrics such as MOTA or IDF1, making the tracking comparison difficult to assess against current practice.

minor comments (7)

[Abstract and throughout] The manuscript contains numerous typos, including 'A lot a research', 'Inspite', 'RetianNet' in Table 2, and 'variablitiy' in §3.1; these should be corrected.
[§3.2] The anchor scales are written as '20, 21/3, 22/3' and '0.1, 0.25, 0.5, 1, 21/3, 2.2'; if these denote 2^{1/3} and 2^{2/3}, the superscripts should be typeset correctly and the intended values stated unambiguously.
[Table 3] The RetinaNet baseline is labeled 'Retinanet [27]', but reference [27] is Li et al.; the Focal Loss paper should be cited as [31]. The citation numbering should be fixed throughout.
[§4.4 and Table 2] The text claims an AR_max=500 of 31.49% for the dense-scales model, while Table 2 reports 30.49%; one of these is a typographical error.
[§4.1] The dataset description says it 'consists of 6,471 images in the training set and 548 images' and does not complete the description of the validation and test splits.
[§3.1] There is an unresolved placeholder citation: 'the same training parameters as mentioned in [?]'.
[Tables 1-4] No error bars or multiple-run statistics are reported, which is important given the small AP differences discussed in §4.4.

Circularity Check

0 steps flagged · score 0.0 of 10

No derivation-level circularity; the ablation is confounded but the claims do not reduce to their inputs by construction.

full rationale

The paper's detection chain is empirical: it chooses anchor scales from VisDrone object size statistics, adds SE blocks, and reports AP/AR on the same validation split. This is in-domain hyperparameter tuning, not a prediction that reduces to a fitted input; the tables are performance measurements, not derived quantities. The tracking module uses an independently trained Deep Association network on COCO [46,47], so the deep cosine metric is external and not refitted on VisDrone MOT; no self-citation chain or uniqueness theorem is load-bearing. The main methodological weakness is that the ablations in Section 3.5 vary anchor scales, SE blocks, epochs, and batch size simultaneously ('The base RetinaNet model was trained for 26 epochs... batch size of 4... model with improved scales ... 25 epochs... batch size of 4... model having new scales along with the SE blocks was trained for 27 epochs... batch size of 2'), and Section 4.3 gives a third schedule ('We train the network for 50K iterations with the batch size set to 1'). This means the AP gain from 14.45 to 17.19 cannot be cleanly attributed to anchors and SE, and Table 4 inherits the attribution problem; but confounding is a correctness risk, not a circularity. The paper even acknowledges transfer limitations ('Although the trained Detector performs well on validation set, it performs sub-optimally on the test set'), which is consistent with benchmark engineering rather than circular derivation.

Assumptions & free parameters 5 free parameters · 2 assumptions · 0 invented entities

The central claim rests on two domain-transfer assumptions: COCO-trained features work for VisDrone, and the validation split predicts official test performance. The detection improvements also depend on hand-chosen anchor scales and inference thresholds. There are no invented entities.

free parameters (5)

anchor scales = 0.1, 0.25, 0.5, 1, 2^(1/3), 2.2
Chosen by hand to cover small object sizes in VisDrone; central to the claimed detection gain.
input image size for training = 1500x1000
Upsampled input size for training; affects scale and compute.
detector confidence threshold = 0.05
Used for both training decoding and test inference; influences precision-recall tradeoff.
NMS threshold = 0.5
Class-wise NMS threshold, affects final detections.
max detections = 500
Evaluation cap used in metrics; the paper emphasizes gains as maxDets grows.

assumptions (2)

domain assumption COCO pretrained weights and COCO-trained association features transfer to VisDrone aerial imagery.
The detection network is initialized from COCO weights and the association network is trained on COCO patches (Sections 4.3, 3.4).
domain assumption The self-created validation split of VisDrone is representative of the official test distribution.
Architecture choice is made on the validation split (Section 4.1); official test numbers in Table 3 contradict this assumption for AP.

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Cite this review

Pith. "Pith review of Aerial multi-object tracking by detection using deep association networks." pith.science (2026). https://pith.science/paper/WJODLMHM

@misc{pith2026190901547,
  author       = {Pith},
  title        = {Pith review of: Aerial multi-object tracking by detection using deep association networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WJODLMHM}},
  note         = {Machine review of arXiv:1909.01547}
}

read the original abstract

A lot a research is focused on object detection and it has achieved significant advances with deep learning techniques in recent years. Inspite of the existing research, these algorithms are not usually optimal for dealing with sequences or images captured by drone-based platforms, due to various challenges such as view point change, scales, density of object distribution and occlusion. In this paper, we develop a model for detection of objects in drone images using the VisDrone2019 DET dataset. Using the RetinaNet model as our base, we modify the anchor scales to better handle the detection of dense distribution and small size of the objects. We explicitly model the channel interdependencies by using "Squeeze-and-Excitation" (SE) blocks that adaptively recalibrates channel-wise feature responses. This helps to bring significant improvements in performance at a slight additional computational cost. Using this architecture for object detection, we build a custom DeepSORT network for object detection on the VisDrone2019 MOT dataset by training a custom Deep Association network for the algorithm.

Figures

Figures reproduced from arXiv: 1909.01547 by the authors.

**Figure 1.** Detection Network ages, the traditional CNN-based methods tend to miss such densely distributed small objects. In this paper, we provide a novel multi-object tracking by detection framework particularly for aerial images captured by drones. We detect ten predefined categories of objects (i.e., pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle) in drone images collected for VisDr… view at source ↗

**Figure 2.** Tracking Network the model is unable to identify such small objects. To address this issue,we modify the anchor parameters to cover the range of sizes of objects in the dataset. While we use the same anchor sizes, anchor aspect ratios and strides for the anchors, we use the scales 0.1, 0.25, 0.5, 1, 21/3, 2.2, which cover a larger variance in size as well as are denser due the use of 6 scales instead of the origina… view at source ↗

**Figure 3.** Qualitative Results Method \AP@IoU 0.50:0.95 0.50 0.75 Yolo v3 13.8 30.43 11.18 RetinaNet 14.45 23.74 15.14 RetinaNet (dense scales) 15.39 33.13 13.07 RetinaNet (dense scales +SE attention) 17.19 37.69 13.97 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

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