Pith. sign in

REVIEW 3 major objections 5 minor 34 references

Mining Platoon Patterns from Traffic Videos

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read Gap-tolerant vehicle groups in traffic video can be enumerated completely, without a verification stage.

desk verdict Real bug in the root pruning rule drops maximal patterns; the VPlatoon idea and MaxGrowth enumeration are otherwise sound and worth a serious look. read the letter →

arxiv 2412.20177 v2 pith:W2NOMYXC submitted 2024-12-28 cs.CV cs.DB

Yijun Bei , Teng Ma , Dongxiang Zhang , Sai Wu , Kian-Lee Tan , Gang Chen This is my paper · ORCID
classification cs.CVcs.DB
keywords VPlatoonco-movementpatternmininggap-tolerantMaxGrowthtrafficvideosurveillancemaximalpruningtrajectoryrecoveryconvoy
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 addresses a practical failure of co-movement pattern mining from surveillance video: previous definitions require a group of vehicles to appear at every camera along a common route, so a single occlusion or identity swap erases a true pattern from the recovered trajectories. The authors define VPlatoon, a relaxed pattern in which the group may skip up to $d$ cameras along the route while still requiring at least $m$ objects, a route of length at least $k$, and temporal proximity $\epsilon$ at each camera on the route. They then claim that all maximal VPlatoon patterns can be enumerated directly by MaxGrowth, a depth-first search over feasible sequences of camera clusters, with no separate validity-verification stage and with two pruning rules that remove non-maximal candidates during enumeration. If correct, this means urban-scale traffic-video analysis can tolerate imperfect tracking without a costly repair step, and the paper reports speedups of up to two orders of magnitude over a filter-and-refine baseline.

What carries the argument

The load-bearing object is the feasible cluster sequence. A cluster $CL=(O,c)$ is a group of at least $m$ objects that are $\epsilon$-close at camera $c$; a sequence $S=[CL_1,\ldots,CL_n]$ is feasible when each appended cluster admits at least $m$ core objects---objects present in all clusters of $S$ whose positions in their own travel paths advance by at most $d+1$ camera positions---so that the sequence's camera list is a $d$-subpath of each core object's travel path. Lemma 2 states that a feasible sequence is exactly a candidate VPlatoon pattern with object set $\lambda(S)$ and route equal to the camera sequence of $S$, which lets MaxGrowth enumerate patterns by growing feasible sequences rather than by generating candidates and then checking validity. Two pruning rules carry the maximality argument: root pruning removes subtrees that can only reproduce objects already covered by an earlier route, and dependency pruning removes an appended cluster whose continuation duplicates a subtree reachable through another feasible cluster.

What would settle it

Open the proof of Lemma 4 and check whether it ever verifies the time-interval containment clause of Definition 2; it verifies only camera order and gap positions. A concrete test is to construct two objects whose camera sequences and camera-wise entrance times satisfy the cluster feasibility conditions but whose full travel-path time intervals overlap while neither contains the other; MaxGrowth still enumerates $\langle \{o_1,o_2\}, P\rangle$ as valid, even though Definition 2's containment condition is undefined or false for the camera-only route $P$.

Watch

Extended reading notes

Core claim

The central claim is an equivalence that removes the need for verification: every valid VPlatoon pattern corresponds to a feasible sequence of clusters, where a cluster is a set of at least $m$ objects that are $\epsilon$-close at one camera, and feasibility encodes the gap tolerance through position advances of at most $d+1$ cameras in each member's travel path. MaxGrowth grows cluster sequences depth-first, and because every grown sequence is feasible by construction, the associated pattern $R(S)=\langle \lambda(S), CL_1.c \to CL_2.c \to \cdots \to CL_n.c\rangle$ is automatically valid; Lemmas 2 and 4 back the claim that the enumeration is both sound and complete, stated as Theorem 1. Two pruning rules remove non-maximal candidates during enumeration: the root pruning rule discards search subtrees whose cluster object sets are already contained in the core objects of an earlier sequence, and the dependency pruning rule discards an appended cluster when continuing through it duplicates a subtree reachable through another feasible cluster. On real video data, the relaxed definition recovers more ground-truth patterns than the previous consecutive-camera definition, and MaxGrowth runs up to two orders of magnitude faster than the extended baseline.

Load-bearing premise

The load-bearing premise is that the common route $P$ of a VPlatoon pattern can be checked as a $d$-subpath of each object's travel path, but Definition 2 defines $d$-subpath only for travel paths that carry time intervals and requires the time interval of one path to be contained in the other, while the $P$ in Definition 4 is a bare camera sequence with no time interval.

Editorial extensions

If this is right

  • Setting $d=0$ makes VPlatoon coincide with the original consecutive-camera VConvoy problem, so MaxGrowth also solves that earlier problem while skipping its verification stage.
  • Because MaxGrowth grows only feasible sequences, it never materializes false-positive candidate patterns; the paper's breakdown shows that the baseline's bottleneck is validness verification, a cost MaxGrowth eliminates.
  • Larger gap tolerance $d$ raises recall at some cost in precision, giving practitioners a single explicit knob for trading pattern completeness against false positives when trajectory recovery is imperfect.
  • The two pruning rules remove most dominated candidates during enumeration; on one large dataset the number of non-maximal patterns drops from roughly sixteen million to about two thousand.

Reading between the lines

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

  • Going beyond the paper: nothing in the cluster-sequence argument depends on cameras specifically, so the same gap-tolerant, verification-free enumeration should transfer to GPS traces or other timestamped location sequences with missing observations.
  • Going beyond the paper: the root pruning rule relies on the precedence relation between clusters, and if a dataset has many vehicles moving in opposite directions through the same cameras, symmetric precedence pairs could become more common than the under-ten-percent rate reported here and reduce the rule's effectiveness.
  • Going beyond the paper: because VPlatoon only diverges from VConvoy where cameras are skipped, comparing the two pattern sets on the same data could be used as a diagnostic to locate the cameras or time windows where occlusion or ID switching is corrupting the recovered trajectories.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper studies co-movement pattern mining from surveillance video trajectories. It relaxes the prior VConvoy definition by allowing gaps in the common camera route (VPlatoon), introduces a parameter d for gap tolerance, and proposes an algorithm called MaxGrowth that enumerates cluster sequences directly, avoiding the filter-and-refine verification stage. The authors also design two pruning rules for maximal patterns and evaluate the approach on semi-synthetic GPS-derived datasets and the real CityFlow video dataset, reporting large speedups over a baseline and improved F1 scores on imperfectly recovered trajectories.

Significance. If correct, the paper would make a useful practical contribution: it targets a real failure mode of video-based trajectory extraction (occlusion and ID switches), proposes a clean relaxed pattern definition, and shows orders-of-magnitude speedups. The idea of representing patterns as sequences of clusters and growing them without a separate verification stage is appealing, and the experiments are extensive. The authors also make the code and data available. However, the central correctness claim of the framework depends on the soundness of the pruning rules, and there is a concrete counterexample showing that the root pruning rule can drop a maximal pattern. The formal definitional mismatch in d-subpath for camera-only routes also needs attention. These issues are load-bearing and require revision before the completeness and maximality claims can be accepted.

major comments (3)
  1. [Section 3 (Definitions 2 and 4)] Definition 2 defines d-subpath only for travel paths, both carrying time intervals, and requires the time interval of the subpath to be contained in the time interval of the superpath. In Definition 4, the common route P is a bare camera sequence with no time interval, so the condition 'P is a d-subpath of P_i' is formally undefined. Lemma 4 uses this condition to derive the position inequalities but does not address the missing time-containment check. The paper should either attach time intervals to P (for instance, derived from the objects' entrance timestamps) or add a camera-only variant of Definition 2 that explicitly drops the time containment condition. Without this, the formal semantics of VPlatoon patterns and of the maximality relation in Definition 5 are not well-defined.
  2. [Section 6.1 (Lemma 5 and Rule 1)] The root pruning rule is unsound. Consider m=2, k=2, d=1 with three objects: o1 and o2 follow A→B→D, and o3 follows B→D→A; all three are ε-close at every camera they meet. Let CL_a=(A,{o1,o2,o3}), CL_i=(B,{o1,o2,o3}), CL_j=(D,{o1,o2,o3}). CL_i is feasible for S=[CL_a] because o1 and o2 satisfy the gap condition, and CL_i.O⊆λ(S)={o1,o2,o3}. Rule 1 therefore prunes the entire subtree rooted at CL_i. However, [CL_i,CL_j] is feasible and R=⟨{o1,o2,o3},B→D⟩ is a valid pattern with |P|=2 and |O|=3. It is maximal: the object set cannot be enlarged, and B→D cannot be extended to A→B→D because o3 visits A only after D, nor to B→D→E because o1 and o2 do not visit E. Hence a maximal pattern is pruned, contradicting Theorem 1. The proof of Lemma 5 asserts that CL_i.O⊆λ(S) implies the core objects after prepending S are unchanged, but this only guarantees membership in λ(S); it does not guarantee that every object in CL_i.O satisfies 0<p_i^c−p_n^c≤d+1, which is required for the object to remain core after prepending. Feasibility of CL_i for S requires this only for at least m objects, so objects like o3 in the counterexample are lost when S is prepended. The rule must be strengthened (for example, by requiring the gap condition for all objects in CL_i.O) or removed.
  3. [Section 5.2 (Lemma 4 and Theorem 1)] The completeness proof of Lemma 4 assumes that every valid pattern gives a feasible cluster sequence by taking clusters that contain O at each camera of P. This step is correct under the intended camera-order-with-gap semantics, but it relies on the undefined d-subpath relation discussed in the first major comment. Moreover, even if Definition 4 is repaired, the completeness of the overall algorithm as stated in Theorem 1 depends on the pruning rules, and Rule 1 as shown above can suppress a feasible sequence that corresponds to a maximal pattern. The authors should either prove completeness for the pruning-aware enumeration or report results with the corrected pruning condition; the current statement of Theorem 1 is not supported by the given lemmas.
minor comments (5)
  1. [Section 3 (Definition 8)] Definition 8 defines a feasible sequence only when the sequence has length at least 2, since it refers to the last cluster's feasibility for the prefix. The base case of a single cluster is used implicitly in Examples 9 and 11 and in the Growth routine; it should be stated explicitly.
  2. [Section 3 (Example 2)] Example 2 writes 'c_3^4 − c_3^2 ≤ 2' and 'c_3^2 − c_3^1 ≤ 2', which compares camera IDs rather than positions. The inequalities should refer to the positions in the mapping, e.g., f(3)−f(2)≤d+1.
  3. [Figures 5-11] Several figure axis labels appear as rendering artifacts (e.g., '/uni00000016/uni00000017/...') and need to be regenerated.
  4. [Various] There are minor typos and grammatical issues: 'suppoert' in Section 3; 'we can also a common object' in Definition 9; 'Specically' in Section 7.4; and 'The final step of the Growth routine' should be 'In the final step...'.
  5. [Algorithm 1] Line 9 of Algorithm 1 iterates over a camera index range '{c_{i−1−d},...,c_{i−1}}' that is not clearly restricted to cameras present in the candidate sequence Seq; this should be clarified or bounded.

Circularity Check

1 steps flagged · score 6.0 of 10

VPlatoon's reported F1 advantage is partly circular because d and the IoU matching threshold are tuned on the same CityFlow ground truth used for the headline comparison; the formal MaxGrowth derivation is independent.

  1. fitted input called prediction [Section 7.1 (Parameter Setup) and Section 7.4 (Effectiveness Analysis, Table 6, Figures 12-13)]
    "As to the pattern parameters, we set the minimum number of objects m to 2, the minimum route length k to 3, the proximity threshold ε to 60 seconds, and the distance parameter d for VPlatoon to 3. ... As d increases, the precision gradually decreases while the recall increases, with the optimal F1-score achieved at d = 3. ... In our default settings, the positive matching thresholds for performance evaluation are set at 80%. ... the largest proportion of discovered patterns having an IoU around 0.8."

    The paper chooses d=3 because Figure 13 shows that this value maximizes F1 on the same CityFlow ground-truth patterns used to compute the headline F1 in Table 6, and it chooses the 80% IoU positive-match threshold because the distribution of the discovered patterns being evaluated peaks at 0.8. The reported VPlatoon F1 of 0.840 versus VConvoy 0.719 is therefore the score at a hyperparameter and an evaluation threshold both fitted to the test data, not an independent predictive comparison. The effectiveness claim is partly forced by this selection procedure, although the formal enumeration equivalence in Lemmas 2-4 remains an independent derivation.

full rationale

The formal derivation of MaxGrowth's correctness and completeness (Lemmas 2-4, Theorem 1) is self-contained: Lemma 2 proves the equivalence between feasible cluster sequences and valid patterns, Lemma 4 maps any valid pattern back to an enumerated feasible sequence, and neither proof depends on a fitted quantity or on an external self-citation. The citation to [30] is used for the baseline TCS-tree and the final non-maximal removal routine, but the core enumeration argument does not reduce to it. The main circularity is in the empirical validation: d=3 and the 80% IoU matching threshold are both selected on the same CityFlow ground truth that is then used to report the headline F1 advantage, so the effectiveness comparison is at least partly an in-sample optimum. Two non-circular soundness defects should also be flagged: Definition 4 requires the common route P to be a d-subpath of each travel path, but Definition 2 defines d-subpath only for travel paths carrying time intervals and P has none; and Lemma 5/Rule 1 is contradicted by a concrete counterexample (m=2, k=2, d=1), so Theorem 1's completeness claim is not actually established by the paper's proof. These defects lower confidence in the formal claims but are not circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No fitted constants enter the algorithm itself; m, k, d, and epsilon are user query parameters. The hand-tuned values appear only in the effectiveness evaluation (d=3 and the 80% IoU threshold). The main unstated assumption is the undefined time-interval condition for d-subpaths whose first argument is a camera-only pattern route.

free parameters (2)
  • d (gap tolerance) = 3 in CityFlow effectiveness evaluation, selected from d=1..4 sweep
    User-specified relaxation parameter; the headline F1=0.840 uses the value that maximizes F1 on the test set, so the reported effectiveness is partly tuned.
  • Positive-match IoU threshold = 0.8
    Set after inspecting the IoU distribution of discovered patterns; directly determines whether a discovered pattern counts as a positive match in the effectiveness evaluation.
assumptions (4)
  • ad hoc to paper A common route P in Definition 4 can be a d-subpath of a travel path even though P carries no time interval, making the containment condition of Definition 2 vacuous.
    Definition 4 condition (2) invokes d-subpath for a bare camera sequence; Lemma 4 uses only camera-order and gap positions and never checks time-interval containment.
  • domain assumption Each travel path lists cameras in strictly increasing entrance time and each camera appears once.
    Definition 1 imposes s_j < s_{j+1}; the injection in Definition 2 and the position differences p_i_j used in Section 5 assume order and uniqueness.
  • domain assumption Trajectory recovery algorithms can be treated as a black-box preprocessing step producing camera-level travel paths.
    Section 3 states that travel paths can be extracted via trajectory recovery algorithms [10,21]; all subsequent mining operates on those paths.
  • ad hoc to paper All epsilon-close clusters required by Definition 6 can be enumerated in advance; the exact cluster enumeration procedure is not specified.
    Algorithm 2 line 2 says 'compute all clusters' but does not describe how maximal object groups per camera are formed, which is needed for Lemma 4's completeness.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Mining Platoon Patterns from Traffic Videos." pith.science (2026). https://pith.science/paper/W2NOMYXC

@misc{pith2026241220177,
  author       = {Pith},
  title        = {Pith review of: Mining Platoon Patterns from Traffic Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W2NOMYXC}},
  note         = {Machine review of arXiv:2412.20177}
}
read the original abstract

Discovering co-movement patterns from urban-scale video data sources has emerged as an attractive topic. This task aims to identify groups of objects that travel together along a common route, which offers effective support for government agencies in enhancing smart city management. However, the previous work has made a strong assumption on the accuracy of recovered trajectories from videos and their co-movement pattern definition requires the group of objects to appear across consecutive cameras along the common route. In practice, this often leads to missing patterns if a vehicle is not correctly identified from a certain camera due to object occlusion or vehicle mis-matching. To address this challenge, we propose a relaxed definition of co-movement patterns from video data, which removes the consecutiveness requirement in the common route and accommodates a certain number of missing captured cameras for objects within the group. Moreover, a novel enumeration framework called MaxGrowth is developed to efficiently retrieve the relaxed patterns. Unlike previous filter-and-refine frameworks comprising both candidate enumeration and subsequent candidate verification procedures, MaxGrowth incurs no verification cost for the candidate patterns. It treats the co-movement pattern as an equivalent sequence of clusters, enumerating candidates with increasing sequence length while avoiding the generation of any false positives. Additionally, we also propose two effective pruning rules to efficiently filter the non-maximal patterns. Extensive experiments are conducted to validate the efficiency of MaxGrowth and the quality of its generated co-movement patterns. Our MaxGrowth runs up to two orders of magnitude faster than the baseline algorithm. It also demonstrates high accuracy in real video dataset when the trajectory recovery algorithm is not perfect.

Figures

Figures reproduced from arXiv: 2412.20177 by the authors.

Figure 1
Figure 1. An illustrative example for relaxed co-movement [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Travel paths for four objects {𝑜1, 𝑜2, 𝑜3, 𝑜4}. Definition 1. Travel Path The travel path 𝑃𝑖 of an object 𝑜𝑖 ∈ O is defined as a sequence of surveillance cameras with associated time intervals: 𝑃𝑖 = (𝑐1, [𝑠1, 𝑒1]) → (𝑐2, [𝑠2, 𝑒2]) → . . . → (𝑐𝑛, [𝑠𝑛, 𝑒𝑛]) where (𝑐𝑗 , [𝑠𝑗 , 𝑒𝑗]) indicates that 𝑜𝑖 is captured by camera 𝑐𝑗 during time interval [𝑠𝑗 , 𝑒𝑗] with 𝑠𝑗 < 𝑒𝑗 and 𝑠𝑗 < 𝑠𝑗+1. We call𝑠𝑗 and 𝑒𝑗 the entrance and exit… view at source ↗
Figure 3
Figure 3. Example clusters under parameters 𝑚 = 2 and 𝜖 = 6. 𝜖, the core objects of 𝜆(𝑆) remain within the same cluster, which ensures that they are 𝜖-close at each camera. Finally, we analyze the parameter 𝑑. For each core object 𝑜𝑐 ∈ 𝜆(𝑆), we can define an injec￾tion function 𝑓 : {1, 2, . . . , 𝑛} → {𝑝 1 𝑐 , 𝑝2 𝑐 , . . . , 𝑝𝑛 𝑐 } from the common route 𝐶𝐿1.𝑐 → . . . → 𝐶𝐿𝑛.𝑐 to 𝑜𝑐 ’s travel path 𝑃𝑐 , such that the common rout… view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: The search tree of MaxGrowth under parameters [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 6
Figure 6. Figure 6: Varying 𝜖. Varying k. As depicted in [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Varying k. Varying d. Recall that we introduce an additional parameter 𝑑 in this work to allow missing cameras for relaxed co-movement pattern mining. When 𝑑 = 0, the problem is reduced to traditional co-movement pattern mining. FRB demonstrates comparable perfor￾mance…
Figure 5
Figure 5. Figure 5: Varying 𝑚. Varying 𝜖. We examine the performance with increasing 𝜖 in Fig￾ure 6. MaxGrowth consistently outperforms FRB mainly because FRB requires heavy computation overhead to verify the constraint of temporal proximity. In contrast, the candidate enumeration scheme …
Figure 8
Figure 8. Figure 8: Varying d. 1500 1700 1900 2100 2300 2500 2700 Number of Objects 10 100 Total Elapsed Time (s) FRB MaxGrowth (a) Singapore 500 2500 4500 6500 8500 1050012000 Number of Objects 1 10 2 10 4 Total Elapsed Time (s) FRB MaxGrowth (b) Chengdu [PITH_FULL_IMAGE:figures/full_fi…
Figure 9
Figure 9. Figure 9: Varying object number. gap is widened with larger-scale dataset. In Chengdu, the running time of FRB is two orders of magnitude higher than MaxGrowth. Varying Path Length. In [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Varying path length. Varying Camera Number. In the final scalability experiment, we report the mining performance w.r.t. the number of cameras. As shown in [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 12
Figure 12. Figure 12: Distribution of IoU values for discovered patterns. [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 13
Figure 13. Figure 13: Pattern quality with varying 𝑑. In the next examination, we examine the sensitivity to the ac￾curacy of offline trajectory recovery algorithms. Specically, we inject noise into the recovered trajectories to control the degree of IDF1 [18], which is a popular metric fo…
Figure 16
Figure 16. Figure 16: The visualization of a VPlatoon pattern from [PITH_FULL_IMAGE:figures/full_fig_p013_16.png]
Figure 14
Figure 14. Figure 14: Pattern quality with varying accuries of recovered [PITH_FULL_IMAGE:figures/full_fig_p013_14.png]
Figure 15
Figure 15. Figure 15: The visualization of a VPlatoon pattern from [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 29 canonical work pages

  1. [1]

    Marc Benkert, Joachim Gudmundsson, Florian Hübner, and Thomas Wolle. 2008. Reporting flock patterns. Computational Geometry 41, 3 (2008), 111–125. https: //doi.org/10.1016/j.comgeo.2007.10.003

    work page doi:10.1016/j.comgeo.2007.10.003 2008

  2. [2]

    Daren Chao, Yueting Chen, Nick Koudas, and Xiaohui Yu. 2023. Track merging for effective video query processing. In 2023 IEEE 39th International Conference on Data Engineering (ICDE) . IEEE, 164–176

    work page 2023

  3. [3]

    Lu Chen, Yunjun Gao, Ziquan Fang, Xiaoye Miao, Christian S Jensen, and Chen- juan Guo. 2019. Real-time distributed co-movement pattern detection on stream- ing trajectories. Proceedings of the VLDB Endowment 12, 10 (2019), 1208–1220

    work page 2019

  4. [4]

    Lu Chen, Yunjun Gao, Xinhan Li, Christian S Jensen, and Gang Chen. 2017. Effi- cient Metric Indexing for Similarity Search and Similarity Joins.IEEE Transactions on Knowledge and Data Engineering 29, 3 (2017), 556–571

    work page 2017

  5. [5]

    Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taixé. 2020. Mot20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003 (2020)

    arXiv 2020

  6. [6]

    Xin Ding, Lu Chen, Yunjun Gao, Christian S Jensen, and Hujun Bao. 2018. Ul- TraMan: A unified platform for big trajectory data management and analytics. Proceedings of the VLDB Endowment 11, 7 (2018), 787–799

    work page 2018

  7. [7]

    Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al. 1996. A density- based algorithm for discovering clusters in large spatial databases with noise. In kdd, Vol. 96. 226–231

    1996

  8. [8]

    Qi Fan, Dongxiang Zhang, Huayu Wu, and Kian-Lee Tan. 2016. A general and parallel platform for mining co-movement patterns over large-scale trajectories. Proceedings of the VLDB Endowment 10, 4 (2016), 313–324

    work page 2016

Show all 34 references
  1. [9]

    Joachim Gudmundsson and Marc Van Kreveld. 2006. Computing longest duration flocks in trajectory data. In Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems . 35–42

    2006

  2. [10]

    Yuhang He, Jie Han, Wentao Yu, Xiaopeng Hong, Xing Wei, and Yihong Gong

  3. [11]

    Hoyoung Jeung, Heng Tao Shen, and Xiaofang Zhou. 2008. Convoy queries in spatio-temporal databases. In 2008 IEEE 24th International Conference on Data Engineering. IEEE, 1457–1459

    2008

  4. [12]

    Jensen, and Heng Tao Shen

    Hoyoung Jeung, Man Lung Yiu, Xiaofang Zhou, Christian S. Jensen, and Heng Tao Shen. 2008. Discovery of convoys in trajectory databases. Proc. VLDB Endow. 1, 1 (aug 2008), 1068–1080. https://doi.org/10.14778/1453856.1453971

    2008

  5. [13]

    Panos Kalnis, Nikos Mamoulis, and Spiridon Bakiras. 2005. On discovering moving clusters in spatio-temporal data. In Advances in Spatial and Temporal Databases: 9th International Symposium, SSTD 2005, Angra dos Reis, Brazil, August 22-24, 2005. Proceedings 9 . Springer, 364–381

    2005

  6. [14]

    Yuxuan Li, James Bailey, and Lars Kulik. 2015. Efficient mining of platoon patterns in trajectory databases. Data & Knowledge Engineering 100 (2015), 167–187

    2015

  7. [15]

    Zhenhui Li, Bolin Ding, Jiawei Han, and Roland Kays. 2010. Swarm: Mining relaxed temporal moving object clusters. Proceedings of the VLDB Endowment 3, 1-2 (2010), 723–734

    2010

  8. [16]

    Yiyang Liu, Hua Dai, Bohan Li, Jiawei Li, Geng Yang, and Jun Wang. 2021. ECMA: an efficient convoy mining algorithm for moving objects. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 1089–1098

    2021

  9. [17]

    Faisal Moeen Orakzai, Toon Calders, and Torben Bach Pedersen. 2019. k/2-hop: fast mining of convoy patterns with effective pruning. Proceedings of the VLDB Endowment 12, 9 (2019), 948–960

    2019

  10. [18]

    Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi

  11. [19]

    Peize Sun, Jinkun Cao, Yi Jiang, Zehuan Yuan, Song Bai, Kris Kitani, and Ping Luo. 2022. Dancetrack: Multi-object tracking in uniform appearance and diverse motion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 20993–21002

    2022

  12. [20]

    Zheng Tang, Milind Naphade, Ming-Yu Liu, Xiaodong Yang, Stan Birchfield, Shuo Wang, Ratnesh Kumar, David Anastasiu, and Jenq-Neng Hwang. 2019. Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification. In Proceedings of the IEEE/CVF ...

    2019

  13. [21]

    Panrong Tong, Mingqian Li, Mo Li, Jianqiang Huang, and Xiansheng Hua. 2021. Large-scale vehicle trajectory reconstruction with camera sensing network. In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking. 188–200

    2021

  14. [22]

    Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Jieping Ye, and Ke Xu. 2018. A unified approach to route planning for shared mobility. Proceedings of the VLDB Endowment 11, 11 (2018), 1633

    2018

  15. [23]

    Gaoang Wang, Yizhou Wang, Renshu Gu, Weijie Hu, and Jenq-Neng Hwang

  16. [24]

    Jianyong Wang and Jiawei Han. 2004. BIDE: Efficient mining of frequent closed sequences. In Proceedings. 20th international conference on data engineering . IEEE, 79–90

    2004

  17. [25]

    Sheng Wang, Zhifeng Bao, J Shane Culpepper, Timos Sellis, and Xiaolin Qin

  18. [26]

    Yida Wang, Ee-Peng Lim, and San-Yih Hwang. 2006. Efficient mining of group patterns from user movement data. Data & Knowledge Engineering 57, 3 (2006), 240–282

    2006

  19. [27]

    Munkh-Erdene Yadamjav, Zhifeng Bao, Baihua Zheng, Farhana M Choudhury, and Hanan Samet. 2020. Querying recurrent convoys over trajectory data. ACM Transactions on Intelligent Systems and Technology (TIST) 11, 5 (2020), 1–24

    2020

  20. [28]

    Xifeng Yan, Jiawei Han, and Ramin Afshar. 2003. Clospan: Mining: Closed sequential patterns in large datasets. InProceedings of the 2003 SIAM international conference on data mining . SIAM, 166–177

    2003

  21. [29]

    Xipeng Yang, Jin Ye, Jincheng Lu, Chenting Gong, Minyue Jiang, Xiangru Lin, Wei Zhang, Xiao Tan, Yingying Li, Xiaoqing Ye, et al. 2022. Box-grained reranking matching for multi-camera multi-target tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patte...

    2022

  22. [30]

    Dongxiang Zhang, Teng Ma, Junnan Hu, Yijun Bei, Kian-Lee Tan, and Gang Chen. 2023. Co-Movement Pattern Mining from Videos. Proc. VLDB Endow. 17, 3 (nov 2023), 604–616. https://doi.org/10.14778/3632093.3632119 14

    2023

  23. [2016]

    In European conference on computer vision

    Performance measures and a data set for multi-target, multi-camera tracking. In European conference on computer vision . Springer, 17–35

  24. [2019]

    Proceedings of the VLDB Endowment 13, 1 (2019), 29–42

    Fast large-scale trajectory clustering. Proceedings of the VLDB Endowment 13, 1 (2019), 29–42

    2019

  25. [2020]

    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops

    City-scale multi-camera vehicle tracking by semantic attribute parsing and cross-camera tracklet matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . 576–577

  26. [2022]

    IEEE Transactions on Multimedia 25 (2022), 1256–1268

    Split and connect: A universal tracklet booster for multi-object tracking. IEEE Transactions on Multimedia 25 (2022), 1256–1268

    2022

Pith tools

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