Visual Object Tracking across Diverse Data Modalities: A Review

Guang Dai; Jiazheng Xing; Jingdong Wang; Mengmeng Wang; Shuo Xin; Teli Ma; Xiaojun Hou; Yong Liu

REVIEW 3 major objections 6 minor 293 references

Visual Object Tracking across Diverse Data Modalities: A Review

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A survey maps visual object tracking across seven data modalities, from RGB to LiDAR and language.

desk verdict A genuinely useful modality-organized survey whose reference tables need a primary-source audit before the paper can be trusted as a citation. read the letter →

arxiv 2412.09991 v1 pith:FEZ2K7FD submitted 2024-12-13 cs.CV cs.AI

Mengmeng Wang , Teli Ma , Shuo Xin , Xiaojun Hou , Jiazheng Xing , Guang Dai , Jingdong Wang , Yong Liu This is my paper · ORCID

classification cs.CVcs.AI

keywords visualobjecttrackingmulti-modalRGB-thermalLiDARRGB-Languagedeeplearningbenchmarkssurvey

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 review aims to give a complete map of visual object tracking (VOT) methods organized by data modality rather than by algorithm family alone. It covers three single-modality tracks — RGB video, thermal infrared, and LiDAR point clouds — and four multi-modal combinations — RGB-Depth, RGB-Thermal, RGB-LiDAR, and RGB-Language. The authors claim this is the first survey to include LiDAR-based, RGB-LiDAR, and RGB-Language tracking, and they support the map with abstracted pipeline schemas, benchmark comparisons, and dataset statistics. A reader who wants to know which tracking paradigms exist, which methods inherit them, and what numbers they achieve on standard benchmarks would use this as a reference.

What carries the argument

The central organizing device is a modality-by-modality taxonomy with abstracted pipeline diagrams and named schemas. For RGB trackers the four schemas are DCF, Siamese, ICD, and OST; for multi-modal trackers the three fusion strategies are early, middle, and late fusion. These schemas do the argumentative work: they turn a list of several hundred methods into a small set of inheritance relations, and they let the survey transfer paradigm knowledge across modalities — for example, TIR trackers building on DCF and Siamese, and LiDAR trackers borrowing the Siamese schema before moving to motion-modeling and one-stream Transformers.

What would settle it

Pick any row in Tables 1 through 11, locate the cited paper's reported score, and check whether the numbers match; one confirmed mismatch, such as the MLSSNet entry described in Section 5.2 as a 500-sequence dataset versus the 430 videos listed in Table 12, would show that the reference value of the tables depends on verification against primary sources.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that VOT is best understood from the perspective of data modalities, and that a survey organized that way can be both comprehensive and novel. For single-modality RGB tracking, it abstracts four paradigms: discriminative correlation filters (online-trained filters convolved with search features), Siamese trackers (a shared network matching a template to a search region), instance classification/detection (a network specialized to one target instance), and one-stream Transformers (a single Transformer that jointly extracts features and relates template to search). Thermal trackers are shown to inherit the DCF and Siamese schemas, and LiDAR trackers are shown to follow Siamese, motion-modeling, and one-stream Transformer designs. For multi-modal tracking, the organizing distinction is fusion stage: early fusion at the input, middle fusion at the feature level, or late fusion at the result level. The survey concludes that these taxonomies, together with its benchmark tables, constitute the first systematic reference for the newly emerged LiDAR-based, RGB-LiDAR, and RGB-Language tracking directions.

Load-bearing premise

The benchmark numbers and dataset statistics in the tables are faithful copies of the cited papers, and the modality and fusion taxonomies assign every method to exactly one correct box.

Editorial extensions

If this is right

A newcomer can identify the paradigm of any RGB tracker by matching its pipeline to one of four schemas rather than reading each paper in full.
Multi-modal trackers can be classified by fusion stage, which predicts whether the method requires aligned inputs, learns cross-modal feature interactions, or fuses final predictions.
The benchmark tables provide a single place to compare trackers across LaSOT, TrackingNet, GOT-10k, VOT, KITTI, nuScenes, Waymo, PTB, DepthTrack, RGBT234, LasHeR, and TNL2K, with numbers transcribed from the cited papers.
Identifying OST as the emerging RGB paradigm points to one-stream Transformers as the schema most likely to be transferred to TIR and LiDAR tracking.

Reading between the lines

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

If the modality-first organization is right, the missing next piece is a cross-modal evaluation protocol that reuses the same target categories and metrics across RGB, TIR, and LiDAR, so that paradigm-transfer claims can be tested quantitatively rather than by inspection.
The fusion-stage taxonomy suggests a testable conjecture: middle fusion will keep dominating RGB-Thermal tracking because it offers learnable cross-modal parameters without requiring the strict input alignment that early fusion demands; a meta-analysis of the table entries could check whether late-fusion methods ever surpass middle-fusion ones at similar speed.
The inclusion of RGB-Language tracking implies the field may treat natural-language descriptions as a first-class query channel alongside boxes and point clouds, which would connect VOT to open-vocabulary and referring-expression benchmarks beyond those listed.
Because the survey records FPS alongside accuracy, a reader could use its tables to test whether the OST paradigm's accuracy gains come at a speed cost, a question the paper raises but does not resolve.

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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 paper is a wide-ranging survey of VOT across seven modalities, and it earns its keep as an organizational contribution. The four-paradigm split for RGB trackers (DCF, Siamese, ICD, OST) is a sensible update over older surveys that stop at DCF and Siamese, and the early/middle/late fusion framing for multi-modal methods is clear enough to be useful. The claimed first coverage of LiDAR, RGB-LiDAR, and RGB-Language tracking appears legitimate—I do not know of another survey that bundles these. The development-lineage figures and the sheer number of entries in the method tables will save a newcomer a lot of digging.

The soft spots are real but concentrated in the reference tables, which is a problem because the paper's value is as a reference. The stress-test note is right: Table 12 lists MLSSNet as 430 videos and 200k boxes, while Section 5.2 says 500 sequences and 228k frames; only one of those can be the dataset stat. Worse, the same table lists MMNet and ECO-MM as Thermal datasets, when Sections 3.2 and Table 4 treat both as trackers. That is a method/dataset mix-up, not a style preference. Table 11 also has three-number cells under a two-metric legend for TNL2K. There is also a stray phrase in Section 2.1 about 'eight multiple modalities' when you only cover four. These are transcription- and copy-paste-level defects, but they are exactly the load-bearing surface for a survey whose selling point is being a reliable map.

On substance, the taxonomy holds up on reading. I did not find a misclassification that would break the four-paradigm or fusion-strategy claims. The paper is a review, so there is no new method or measurement to verify, and that is fine given the title.

Who is this for? Practitioners wanting a quick orientation across modalities, and newcomers who need one entry point. It deserves a serious referee, but the referee should require a primary-source audit of Tables 1–12 before publication. I would cite it after that, and I would probably bring it to a reading group as a starting point for discussion.

Referee Report

3 major / 6 minor

Summary. The paper is a survey of visual object tracking (VOT) organized by data modality. It reviews three single-modal families (RGB, thermal infrared, LiDAR) and four multi-modal combinations (RGB-Depth, RGB-Thermal, RGB-LiDAR, RGB-Language). For RGB trackers it proposes a taxonomy of four deep-learning paradigms (discriminative correlation filters, Siamese trackers, instance classification/detection, and one-stream transformers); for TIR and LiDAR it provides modality-specific taxonomies; and for multi-modal methods it applies an early/middle/late fusion categorization. The survey compiles comparison results on many benchmarks in Tables 1-11 and dataset statistics in Table 12, and closes with ten short discussions of future directions such as parameter-efficient transfer learning, online learning, and multi-modal tracking. The paper claims to be the first review covering LiDAR-based, RGB-LiDAR, and RGB-Language VOT methods.

Significance. If the benchmark tables and dataset statistics are reliable, this survey would be a useful entry point for researchers, especially for the less-covered LiDAR, RGB-LiDAR, and RGB-Language areas. The four-RGB-paradigm taxonomy and the early/middle/late fusion categorization are clear organizational principles, and the breadth of coverage (300+ papers, seven modality families) is a genuine strength. Because the survey's main contribution is reference value rather than new methods or derivations, the fidelity of the tables is load-bearing: a reader consulting this paper will use the tables to compare trackers and to select datasets. The paper ships no code or proofs, but as a survey that is not expected; its value rests on accurate transcription of the cited literature.

major comments (3)

[Section 5.2 and Table 12 (Thermal block)] The dataset statistics for MLSSNet are internally contradictory: Section 5.2 states that MLSSNet [293] is a large-scale TIR dataset with 500 video sequences and 228k frames, while Table 12 reports 430 videos, 200k boxes, 20 classes, and an average duration of 15.5s for the same reference. Since the survey's reference value depends on faithful transcription of primary sources, the authors must verify the original paper and make the text and table agree.
[Table 12 and Section 5.2 vs. Section 3.2 and Table 4] References [293] (MLSSNet), [294] (MMNet), and [30] (ECO-MM) are presented as trackers with benchmark results in Table 4 and Section 3.2, but the same references are listed as Thermal datasets in Table 12 and described as datasets in Section 5.2. If these papers indeed introduce both a tracker and a dataset, the survey should explicitly say so and clearly separate the two roles; as it stands, a reader cannot tell whether the rows in Table 12 are datasets, methods, or both, which undermines the dataset table.
[Table 11] The caption of Table 11 states that results are evaluated by Precision/AUC, but several TNL2K cells contain three values (e.g., Feng et al. 0.27/0.34/0.25, Wang et al. 0.06/0.11/0.11 and 0.42/0.50/0.42, VLTTT 0.53/0.53). The legend must be expanded to explain what the third number represents, or the cells must be corrected, because the table is not interpretable as presented.

minor comments (6)

[Section 2.1] The sentence claiming the survey covers "eight multiple modalities" should read "four multiple modalities," since Section 4 reviews exactly four multi-modal combinations (RGB-Depth, RGB-Thermal, RGB-LiDAR, RGB-Language).
[Table 12] LaSOT appears in the RGB block with year 2019 and in the RGB-La block with year 2018; the year should be made consistent, and the double listing (the same dataset in two modality groups) should be explicitly justified.
[Table 1] The last column header appears as "FPSSR(%)", which seems to merge the FPS and SR(%) columns; please split the header into separate columns for FPS and SR(%) or correct the label.
[Section 4.4] The citation for VLTTT appears as "VLTT T[411]" in the text but as "V LTT T[41]" in Table 11; the reference number should be consistent (the reference list entry is [41]).
[Section 5.2] The phrase "most of them are shotted at night" contains a typo; "shotted" should be "shot."
[Section 1] The claim of being the first review to cover LiDAR-based, RGB-LiDAR, and RGB-Language VOT is plausible but should be substantiated by a more explicit comparison with the related surveys listed in Section 2, since the current discussion does not fully rule out partial coverage in prior works.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey's claims are descriptive summaries of external literature, with no derivation whose output depends on its own inputs.

full rationale

This is a survey paper whose central claims are taxonomic organization, method summaries, and transcribed benchmark/dataset statistics. There is no derived quantity, no fitted parameter, and no predictive claim that could reduce by construction to its own inputs. The few self-citations (e.g., Wang et al. [6], [7], [195], [263], [275], [277], [238]) appear in contextual passing remarks such as applications of tracking, lists of DCF variants, video object segmentation, PEFT, and future directions; none of these citations is load-bearing for the survey's stated contributions of organizing VOT methods by modality or reporting comparison results. The taxonomy distinctions (four RGB paradigms; early/middle/late fusion for multi-modal methods) are editorial classifications of external work, not outputs derived from the cited papers in a way that makes the classification equivalent to an input. The internal inconsistencies flagged by a skeptical reading, such as the MLSSNet numbers in Section 5.2 versus Table 12, the dataset-vs-method labeling of MMNet and ECO-MM in Table 12, and the TNL2K metric-legend mismatch in Table 11, are transcription or presentation defects that would affect the survey's reference reliability; they are correctness risks, not circular reasoning. Under the rule that non-consensus or factual errors are not circularity, these do not raise the circularity score. The paper is therefore self-contained as a review and exhibits no significant circularity.

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

No new quantities, models, or entities are introduced; the paper's contribution is organizational. Its load-bearing assumptions are about the fidelity of transcribed benchmark numbers, the completeness of taxonomies, and the precedence of its coverage.

assumptions (3)

domain assumption The performance numbers in Tables 1 through 11 are accurately transcribed from the cited papers.
The survey's comparative value depends on the fidelity of these reported benchmark results; Section 3 and Tables 1 through 11 compile numbers from external papers without independent reproduction.
domain assumption The proposed taxonomies, four RGB paradigms and three fusion strategies for RGB-D and RGB-T methods, are faithful, complete, and non-overlapping.
Sections 3.1 and 4 organize methods into categories; misclassification would mislead readers about paradigm boundaries and the relationships between methods.
ad hoc to paper The claim of being the first review for LiDAR-based, RGB-LiDAR, and RGB-Language VOT is correct.
Sections 1 and 2 assert novelty of coverage without an exhaustive comparison against all earlier surveys, so this precedence claim is unverified.

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Cite this review

Pith. "Pith review of Visual Object Tracking across Diverse Data Modalities: A Review." pith.science (2026). https://pith.science/paper/FEZ2K7FD

@misc{pith2026241209991,
  author       = {Pith},
  title        = {Pith review of: Visual Object Tracking across Diverse Data Modalities: A Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FEZ2K7FD}},
  note         = {Machine review of arXiv:2412.09991}
}

read the original abstract

Visual Object Tracking (VOT) is an attractive and significant research area in computer vision, which aims to recognize and track specific targets in video sequences where the target objects are arbitrary and class-agnostic. The VOT technology could be applied in various scenarios, processing data of diverse modalities such as RGB, thermal infrared and point cloud. Besides, since no one sensor could handle all the dynamic and varying environments, multi-modal VOT is also investigated. This paper presents a comprehensive survey of the recent progress of both single-modal and multi-modal VOT, especially the deep learning methods. Specifically, we first review three types of mainstream single-modal VOT, including RGB, thermal infrared and point cloud tracking. In particular, we conclude four widely-used single-modal frameworks, abstracting their schemas and categorizing the existing inheritors. Then we summarize four kinds of multi-modal VOT, including RGB-Depth, RGB-Thermal, RGB-LiDAR and RGB-Language. Moreover, the comparison results in plenty of VOT benchmarks of the discussed modalities are presented. Finally, we provide recommendations and insightful observations, inspiring the future development of this fast-growing literature.

Figures

Figures reproduced from arXiv: 2412.09991 by the authors.

**Figure 1.** The advantages, disadvantages and applications of the discussed three single-modal VOT and four multi-modal VOT. [14], [15], [16], [17] and large-scale datasets [18], [19], [20]. We mainly focus on the methods of the past decade, especially the methods based on Deep Neural Network (DNN). According to their pipelines, we classify the mainstream RGB trackers into four categories, namely Discriminative Correlation Fil… view at source ↗

**Figure 2.** Develop lineage of representative DCF-based RGB trackers. The methods are divided according to their employed features, including [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Visualization of the four schemas of RGB-based VOT, which [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Develop lineage of representative Siamese-based RGB trackers. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: Develop lineage of representative ICD-based RGB trackers. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 7.** Figure 7: Develop lineage of Thermal-based trackers. The methods are split into Discriminative Correlation Filter, Siamese and Others. HIPTrack [94], RFGM [88], ROMTrack [86], SeqTrack [92], ARTrack [91], VideoTrack [93], ODTrack [95] and EVPTrack [96]. Specifically, leveraging…

**Figure 8.** Figure 8: Develop lineage of LiDAR-based trackers. The methods are split into Siamese, Motion-Modeling and Single-Branch framework, where the first one has dominated till now. Then, we make fine-grained categorization to conclude the common labels of related methods, like using…

**Figure 9.** Figure 9: Develop lineage of RGB-Depth based trackers. We categorize the methods based on the feature fusion manners, which can be summarized into Early Fusion, Middle Fusion, and Late Fusion specifically. and has fewer model parameters and computational overheads. Moreover, wi…

**Figure 10.** Figure 10: Develop lineage of RGB-Thermal based trackers. We categorize the methods based on the feature fusion manners, which can be summarized into Early Fusion, Middle Fusion, and Late Fusion specifically. is of practical value and significance. 4.2 RGB-Thermal Trackers RGB …

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Reference graph

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Transformer tracking,

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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