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REVIEW 4 major objections 5 minor 29 references

RAMOTS: A Real-Time System for Aerial Multi-Object Tracking based on Deep Learning and Big Data Technology

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A Kafka/Spark pipeline can run real-time aerial multi-object tracking at 28 FPS on a single GPU.

desk verdict A useful detector/tracker benchmark on aerial MOT, but the Kafka/Spark 'real-time' claim is not actually measured and the paper contradicts itself on hardware. read the letter →

arxiv 2502.03760 v1 pith:CUUEERPJ submitted 2025-02-06 cs.CV

Nhat-Tan Do , Nhi Ngoc-Yen Nguyen , Dieu-Phuong Nguyen , Trong-Hop Do This is my paper · ORCID
classification cs.CV
keywords multi-objecttrackingUAVvideoApacheKafkaSparkYOLOv8YOLOv10ByteTrackBoTSORT
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 practical UAV multi-object tracking needs not only accurate deep-learning detectors and trackers but also a streaming infrastructure that can ingest, distribute, and process video from many drones. It proposes RAMOTS, which sends UAV footage through Apache Kafka into Apache Spark, where YOLOv8/YOLOv10 detectors and ByteTrack/BoTSORT trackers run per frame and results flow back through Kafka. The authors report that the best configuration, YOLOv8l with BoTSORT, reaches 48.14 HOTA and 43.51 MOTA on VisDrone2019-MOT and 57.21 HOTA on UAVDT while sustaining 28 FPS on one NVIDIA RTX 3060. The contribution is thus an integration rather than a new tracker: evidence that state-of-the-art components can be embedded in a scalable, fault-tolerant streaming system without giving up real-time speed.

What carries the argument

The load-bearing mechanism is the Kafka-to-Spark streaming loop: a Kafka producer sends UAV frames to a consumer, Spark stages run the detector and tracker on each frame, and results are published back through Kafka for delivery to clients. The MOT stage itself is modular, pairing one of several detectors (YOLOv8, YOLOv10, RT-DETR, Faster R-CNN) with one of several trackers (ByteTrack, BoTSORT, SMILETrack-R); the reported best pair is YOLOv8l with BoTSORT. Kafka's partitioning and replication provide fault-tolerant ingestion, while Spark's cluster scheduling provides parallel processing, so the claimed real-time throughput of 28 FPS is supposed to hold even as the number of camera streams grows.

What would settle it

Measure wall-clock time from frame arrival at the Kafka producer to tracked-result output at the consumer on the same RTX 3060 machine, using the paper's YOLOv8l+BoTSORT configuration; if the end-to-end rate falls below 28 FPS (or below real-time video rate) once Kafka ingestion and Spark scheduling are included, the central real-time claim fails.

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Extended reading notes

Core claim

The central claim is that a distributed streaming pipeline built from Apache Kafka and Apache Spark can carry out multi-object tracking on UAV video in real time with accuracy competitive with purpose-built MOT methods. Concretely, the paper reports that combining YOLOv8l detection with BoTSORT association yields a HOTA of 48.14 and a MOTA of 43.51 on the VisDrone2019-MOT test set, and a HOTA of 57.21 on the UAVDT benchmark, while processing at 28 FPS on a single RTX 3060 GPU. The authors read this as showing that the bottleneck for practical aerial MOT is not just algorithm quality but the integration of detection, tracking, and stream processing; their system is designed so that Kafka's partitioning and Spark's distributed computing allow horizontal scaling to more UAV streams.

Load-bearing premise

The real-time claim assumes the quoted 28 FPS is the end-to-end throughput of the full Kafka/Spark pipeline, not just the detector and tracker running on frames; the paper does not state whether streaming overhead is included in that number.

Editorial extensions

If this is right

  • If the reported throughput holds, UAV fleets can stream footage to a server-side pipeline that returns tracks in real time, enabling downstream surveillance, traffic monitoring, and search-and-rescue applications.
  • Accuracy plus speed can be obtained by composing off-the-shelf detectors and trackers rather than designing a new MOT algorithm, since the best configurations in the paper outperform several prior UAV-MOT methods in the comparison tables.
  • BoTSORT consistently improves over ByteTrack by 2% to 5% on the same detector in the paper's experiments, suggesting that camera-motion compensation is valuable for aerial footage.
  • The paper's FPS results indicate the YOLOv8l+BoTSORT combination runs at 28 FPS even on a mid-range GPU (RTX 3060), so real-time aerial tracking does not require high-end server hardware.
  • The architecture scales horizontally by adding Kafka partitions and Spark nodes, which is the paper's proposed route to handling multiple UAVs simultaneously.

Reading between the lines

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

  • The paper leaves the end-to-end throughput measurement ambiguous: the 28 FPS figure appears tied to detector/tracker inference and may not include Kafka serialization, Spark scheduling, and result delivery; a direct end-to-end latency benchmark would settle whether the real-time claim holds for the full pipeline.
  • Because the system reuses generic MOT components, the same Kafka/Spark skeleton could likely host newer detectors or trackers with minimal changes, making the contribution a reusable streaming substrate for aerial vision rather than a fixed model.
  • The reported accuracy comes from models fine-tuned only 4 epochs on VisDrone; longer training or test-time augmentation might close the gap to the top of the VisDrone leaderboard while preserving the streaming architecture.
  • A natural stress test would run multiple simulated UAV streams through one Spark cluster and measure FPS degradation, which the paper motivates but does not execute.
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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 proposes RAMOTS, a real-time multi-object tracking system for UAV video that integrates Apache Kafka and Apache Spark with deep-learning detectors (YOLOv8, YOLOv10, RT-DETR, Faster R-CNN) and trackers (ByteTrack, BoTSORT, SMILETrack-R). The system ingests UAV streams through Kafka, processes frames through a Spark pipeline, and delivers results back via Kafka. The authors report a HOTA of 48.14 and a MOTA of 43.51 on the VisDrone2019-MOT test set, along with a processing speed of 28 FPS on a single GPU, and comparable results on UAVDT. The experimental section compares detector-tracker combinations on these two benchmarks using standard MOT metrics.

Significance. The paper addresses a genuine gap: most UAV MOT research focuses on algorithmic accuracy rather than deployable streaming architectures. The reported accuracy numbers for standard detector-tracker combinations are plausible reference points, and the integration of Kafka/Spark is a reasonable engineering direction. However, the central system-level claim—that the proposed system 'maintains a real-time processing speed of 28 FPS' through the full Kafka/Spark pipeline—is not supported by the reported experiments. No code, cluster configuration, end-to-end latency measurement, or comparison against a no-middleware baseline is provided, so the paper currently reads as an offline MOT benchmarking study plus a high-level architecture sketch.

major comments (4)
  1. [Section IV, Table II, Abstract] The 28 FPS figure is not defined as an end-to-end measurement of the proposed Kafka/Spark pipeline. The FPS column in Table II appears to report per detector-tracker combination speeds, but the paper never states whether these numbers include Kafka ingestion, Spark scheduling, and result delivery. The Abstract and Conclusion attribute this speed to the proposed system, so the measurement boundary must be specified explicitly. Without a latency breakdown or a baseline running the same detector-tracker on the same hardware without Kafka/Spark, the claim that the middleware preserves real-time performance is unsupported.
  2. [Section IV, Implementation Details] The implementation details contain two contradictory hardware descriptions: the text states 'We utilized a single NVIDIA Tesla A100 GPU for training and inference' and then says 'All inferences are run on the same machine with 16GB of RAM, ~3.2GHz CPU, and an NVIDIA RTX 3060 GPU.' This ambiguity makes it impossible to determine which hardware produced the FPS and accuracy numbers in Table II. The authors must state exactly which GPU was used for training, which for inference, and which hardware produced each reported metric.
  3. [Section III.B, Contribution list in Section I] The architecture description is internally inconsistent regarding parallel execution. Section III.B says the Kafka consumer 'sends each frame to the Person Detection model sequentially,' while the contributions claim 'parallel processing of video frames across multiple compute nodes.' Multi-object tracking is stateful: tracklets persist across frames, so distributing frames across Spark workers requires an explicit strategy for maintaining track state and preserving frame order. No such strategy, partitioning scheme, or micro-batch configuration is described. Without this, the scalability claim for the distributed architecture is not substantiated.
  4. [Section IV, Results and Discussion] The comparison in Table I aggregates results from the literature without clarifying whether the hardware, preprocessing, or evaluation protocols are comparable. In particular, the FPS values for literature methods (e.g., 17.8, 18, 17.6) are listed without specifying the GPU or whether they were measured on the same datasets and settings. Since the paper's accuracy comparisons are a secondary claim, this would be acceptable if the primary system-level claim were properly measured, but as it stands the reader cannot assess whether the proposed system improves on prior work in any operational sense.
minor comments (5)
  1. [References] Several references use 'at el' instead of 'et al.' (e.g., references [3], [4], [6], [16], [19], [22], [25], [26], [27], [28], [29]); please correct throughout.
  2. [Section IV, Table II caption] The caption says 'Comparison of different methods with combined detector and tracker' but the table also lists FPS values that are not clearly tied to a measurement protocol; please state in the caption or text what the FPS values represent.
  3. [Figure 3 caption] The caption reads 'Detail comparison are shown in I and II' and should be 'Details are shown in Tables I and II.'
  4. [Section II, Related Works] The related-work discussion of transformer-based detectors (e.g., DETR) lacks citations; please add appropriate references.
  5. [Section IV, Implementation Details] The training protocol is described only as fine-tuning for 4 epochs with batch size 16 and learning rate 0.01; please specify the optimizer, weight decay, input resolution, and whether these settings are identical for all detectors, since the comparisons are only fair if training details match.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: reported HOTA/MOTA/FPS are external benchmark measurements, not derived from fitted inputs; the Kafka/Spark real-time claim is unsupported but not circular.

full rationale

The paper's accuracy numbers (HOTA 48.14, MOTA 43.51 on VisDrone2019-MOT, etc.) are produced by running standard detector-tracker combinations (YOLOv8l/YOLOv10l with ByteTrack/BoTSORT/RT-DETR) against public datasets using standard MOT metrics. There is no fitted parameter that is later renamed as a prediction and no equation in which an output quantity is identical to an input by construction. The claimed novelty is the Kafka/Spark streaming architecture, but the experiments do not actually measure the end-to-end system: Section IV only reports per-method FPS and states all inferences run on a single GPU machine, with contradictory hardware statements (Tesla A100 vs RTX 3060). This is a serious evidentiary gap—the real-time, scalable-system claim is not validated by the reported measurements—but it is a completeness/validity problem, not circular reasoning. The paper contains no load-bearing self-citations; references are to external method papers (ByteTrack, BoTSORT, VisDrone, UAVDT) and no uniqueness theorem or prior author result is invoked to force a conclusion. Therefore the circularity score is 0.

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

The central claims rest on the transfer of pretrained detectors, the transparency of the Kafka/Spark layer, and the comparability of literature baselines. The first two are untested assumptions; the third is standard but should be checked.

free parameters (3)
  • detection_score_threshold = 0.6
    Set in Section IV for both BoTSORT and ByteTrack; it controls which detections enter the association stage and therefore affects HOTA, MOTA, and FPS.
  • IoU rejection threshold = 0.2
    Used in the linear assignment step to reject matches; changing it changes track continuity and identity switches.
  • track_buffer = 30 frames
    Lost tracklets are retained for 30 frames before deletion; this trades latency and computational load against re-identification.
assumptions (3)
  • domain assumption COCO-pretrained YOLO models fine-tuned for 4 epochs on VisDrone2019-DET transfer to VisDrone2019-MOT and UAVDT.
    Section IV states the training recipe; no validation or domain-shift analysis is reported.
  • ad hoc to paper Kafka and Spark act as transparent middleware that does not change detection or tracking outputs and adds no unmeasured latency.
    Section III-B describes the data flow, but no ablation or end-to-end timing proves this.
  • domain assumption Published scores in Table I are comparable to the authors' single GPU runs.
    Table I aggregates results from different papers with different hardware and protocols, and the authors do not rerun those methods.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RAMOTS: A Real-Time System for Aerial Multi-Object Tracking based on Deep Learning and Big Data Technology." pith.science (2026). https://pith.science/paper/CUUEERPJ

@misc{pith2026250203760,
  author       = {Pith},
  title        = {Pith review of: RAMOTS: A Real-Time System for Aerial Multi-Object Tracking based on Deep Learning and Big Data Technology},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CUUEERPJ}},
  note         = {Machine review of arXiv:2502.03760}
}
read the original abstract

Multi-object tracking (MOT) in UAV-based video is challenging due to variations in viewpoint, low resolution, and the presence of small objects. While other research on MOT dedicated to aerial videos primarily focuses on the academic aspect by developing sophisticated algorithms, there is a lack of attention to the practical aspect of these systems. In this paper, we propose a novel real-time MOT framework that integrates Apache Kafka and Apache Spark for efficient and fault-tolerant video stream processing, along with state-of-the-art deep learning models YOLOv8/YOLOv10 and BYTETRACK/BoTSORT for accurate object detection and tracking. Our work highlights the importance of not only the advanced algorithms but also the integration of these methods with scalable and distributed systems. By leveraging these technologies, our system achieves a HOTA of 48.14 and a MOTA of 43.51 on the Visdrone2019-MOT test set while maintaining a real-time processing speed of 28 FPS on a single GPU. Our work demonstrates the potential of big data technologies and deep learning for addressing the challenges of MOT in UAV applications.

Figures

Figures reproduced from arXiv: 2502.03760 by the authors.

Figure 1
Figure 1. Our proposed Real-time UAV Videos Multi-object Tracking System [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. samples of UAVDT and VisDrone dataset. The videos from the two datasets cover various aspects, including location, environment, lighting, [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Comparative performance of Deep Learning-based MOT methods [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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