STAR: Spatial-Temporal Tracklet Matching for Multi-Object Tracking
Authors: Xuewei Bai
Yongcai Wang
Deying Li
Haodi Ping
LI Chunxu
Venue: NeurIPS 2025 poster ()
Keywords: multi-object-tracking
spatial-temporal
graph matching
Primary area: applications
TL;DR: To address viewpoint variations and occlusions, this paper proposes a novel Spatial-Temporal Tracklet Graph Matching paradigm (STAR).

Abstract: Existing tracking-by-detection Multi-Object Tracking methods mainly rely on associating objects with tracklets using motion and appearance features. However, variations in viewpoint and occlusions can result in discrepancies between the features of current objects and those of historical tracklets. To tackle these challenges, this paper proposes a novel Spatial-Temporal Tracklet Graph Matching paradigm (STAR). The core idea of STAR is to achieve long-term, reliable object association through the association of ``tracklet clips (TCs)". TCs are segments of confidently associated multi-object trajectories, which are linked through graph matching. Specifically, STAR initializes TCs using a Confident Initial Tracklet Generator (CITG) and constructs a TC graph via Tracklet Clip Graph Construction (TCGC). In TCGC, each object in a TC is treated as a vertex, with the appearance and local topology features encoded on the vertex. The vertices and edges of the TC graph are then updated through message propagation to capture higher-order features. Finally, a Tracklet Clip Graph Matching (TCGM) method is proposed to efficiently and accurately associate the TCs through graph matching. STAR is model-agnostic, allowing for seamless integration with existing methods to enhance their performance. Extensive experiments on diverse datasets, including MOTChallenge, DanceTrack, and VisDrone2021-MOT, demonstrate the robustness and versatility of STAR, significantly improving tracking performance under challenging conditions.

STAR: Spatial-Temporal Tracklet Matching for Multi-Object Tracking
Xuewei Bai'
Yongcai Wang!* Deying Li Haodi Ping2t
Chunxu Li
School of Information, Renmin University of China
2 School of Computer Science, Beijing University of Technology
China Waterborne Transport Research Institute
(bai_ xuewei, yew, deyingli) @ @eruc.edu.co
haodi wingspjut.e.eeeeieee en
1 Lichunxu@wti.ac
Abstract
Existing tracking-by-deteetie Multi-Object Tracking methods mainly rely on associating objects with tracklets using motion and appearance features. However, variations viewpoint and occlusions can result in discrepancies between the fea- tures of current objects and those of historical tracklets. To tackle these challenges, this paper proposes novel Spatial- Temppoll Tracklet Graph Matching paradigm (STAR). The core idea of STAR is to achieve long-term, reliable object association through the association Tracklet Clips (TCs). TCs are segments of confidently associated multi-object trajectories, which are linked through graph matching. Specifically, STAR initializes TCs using a Confident Initial Tracklet Generator (CITG) and constructs TC graph via Tracklet Clip Graph Construction (TCGC). In TCGC, each object in a TC is treated as a vertex, with the appearance and local topology features encoded on the vertex. The vertices and edges of the TC graph are then updated through message propagation to capture higher-order features. Finally, Tracklet Clip Graph Matching (TCGM) method proposed to efficiently and accurately associate the TCs through graph matching. STAR is model-agnostic, al- lowing for seamless integration with existing methods enhance their performance. Extensive experiments on diverse datasets, including MOTChallenge, Dance Track, and VisDrone2021-Men
demonstrate the robustness and versatility of STAR, significantly improving tracking performance under challenging conditions. The code is available at hhttt::///// // thub.. www.cow.ccia.eceecccc bww.weweix00-o httpa//eeeiiio---doooooo///t CST
Introduction
Multi-object tracking (MOT) is a longstanding task in computer vision 2 generally divided into two paradigms: -orkinggb--detcttion (TBD) and joint ditection-and-trackin (JDT). Currently, generally outperforms JDT in terms of accuracy. The core task in TBD involves effectively extracting object features and designing accurate association strategies to assign stable IDs the same object. However, existing methods still encounter challenges feature extraction and data association in difficult scenarios, such as crowded environments or frequent occlusions.
TBD 5 6. 7 8
Dr. Wang is supported in part by the National Natural Science Foundation of China Grant 6o. 1o.61972404, Public Computing Cloud, Renmin University of China, and the Blockchain Lab. School of Information, Renmin University China.
Dr. Li is supported in part by the National Natural Science Foundation of China Grant No. No.12071478 1478. Dr. Ping is supported by Beijing Natural Science Foundation under Grant 4244076.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Reliable object association in crowded or occluded scenarios poses challenges for both feature extraction and association. Existing methods utilize a variety of features, including object appearance
[10
11
12 13,
motion [4 15 16
17 18, temporal 19 20 21, and neighborhood topology fea-
tures
22
23
24
25
However, these features may not be sufficiently distinctive in such challenging
environments. For association, methods mainly employ bipartite matching [26 27 28, 29 30 and
graph matching [1 32]. While graph matching provides higher accuracy, its computational cost
increases rapidly with the number of objects and is highly reliant the distinctiveness of features.
To address the challenges of object association in crowded and occluded scenarios, we propose a trAcklet gRaph matching paradigm (STAR). The key aspect of STAR is its ability to maintain reliable long-term object associations, even when objects are occasionally obstructed. The central idea is to enhance feature distinctiveness by utilizing Tracklet Clips instead of individual object instances. A Tracklet Clip (TC) refers to segment of confidently associated trajectories of multiple objects, which captures the appearance, spatial, and temporal features of an object, making it more distinetive in the feature space. Additionally, we further enhance the features of TCs by integrating topology information and high-order features through graph neural networks.
novel
More specifically, STAR consists of three components. (I) (CITG) uses dynamically adjusted IoU-based method to produce initial tracklet segments from the input video, ensuring consistency by adaptively separating tracklets of the same object during occlusion. (2) The Tracklet Clip Graph Construction component rearranges tracklet segments by timestamps, divides them into tracklet clips (TCs), and converts them into feature graphs. Each tracklet within a TC is treated as a vertex, while the relationships between these vertices are modeled as edges. Vertex features integrate appearance, topology, and temporal information, with message propagation used to capture higher-order features. (3) The Tracklet Clip Graph Matching (TCGM) module enables fast and accurate TC association through graph matching. The main contributions of this work are summarized as follows:
The Confident Initial Tracklet Generator
1. STAR innovatively reformulates the object association problem into the construction and matching of Tracklet Clip feature graphs, thus effectively addressing occlusions.
2. The proposed Tracklet Clip Graph Construction (TCGC) method enhances distinctiveness and robustness of each object's feature by constructing multi-object spatial-temporal feature graphs.
3. Our Tracklet Clip Graph Matching (TCGM) approach employs tracklet-level graph match- ing to not only enhance matching accuracy but also overcome the efficiency bottlenecks associated with traditional frame-level graph matching.
MOT20
MOT17
STAR
STAR
GMTrace
79
78
SpackTrack
BASE
75
BPMTreck
GMTracker
BPMTrack
HyleTTaa
Festiresa
IDF1 (%)
ByteTrack
IDF1
76
Omni rack
MAA
GRIU
75
ReMOT
TLR
MO3TR
FairMO
MAA
72
GTR
71
76
79
76
MOTA (%)
MOTA (%)
Figure 1: Comparison MOT17 and MOT20. The x-axis represents MOTA, the y-axis represents IDF1, and the bubble size indicates the number of ID Switches (smaller better).
2

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2
Related Work
MOT has gained significant attention across industry and academia. We review relevant methods focusing on feature extraction and data association approaches.
2.1 Feature Extraction
Features fall into four categories: motion, appearance, temporal, and topology. We examine how existing methods address feature discontinuities caused by viewpoint changes.
Motion Feature. Basic motion features (position, velocity, bounding box dimensions) become unreliable with irregular camera movement. OCSORT 333
modifies the Kalman filter K to
prioritize detection results. StrongSORT (35 and MAT (3 use regression to connect fragmented trajectories. While BoT-SORT [ and MAA [5] compensate for camera motion through coordinate transformations, they remain computationally expensive and struggle in crowded scenes.
Appearance Feature. Deep learning models extract nigh-dimensional appearance vectors 37 that complement motion features. FairMOT [TO] enhances feature extraction, while SimpleTrack 38 and BoT-SORT [1] optimize feature combinations. Xie al. [39] develop region-based networks for fine-grained features. However, these methods perform poorly with UAV footage where object textures are less distinct and undergo perspective deformation.
Temporal Feature. These features model frame-to-frame dependencies. CenterTrack [9] and STTA 20 employ temporal attention, while McLaughlin et al. [40] use recurrent networks. Zhang et al. 21 propose orderless representations for better temporal modeling. These approaches, however, are sensitive to occlusions and often neglect multi-object relationships.
Topology Feature. Graph- based structures capture object relationships. GSM 22
builds directed graphs based on relative positions. GTAN [4 creates graphs between detections and trajectories but overlooks intra-frame relationships. 255 introduces topology features that remain stable under
viewpoint changes but often ignore temporal information.
Existing methods underutilize tracklet-level features, limiting their effectiveness against occlusions and viewpoint changes.
2.2 Data Association
Data association connects current object features with previous tracklet features through bipartite or graph matching.
Bipartite Matching.
This approach treats association linear assignment problem. ByteTrack Track uses thresholding while retaining low-confidence detections. This two-stage association has become standard (used in [26 27
41
Some methods extend this approach: [29] uses three-stage association, while JG-Track 30 employs four stages. However, multi-stage approaches that don t
17 28
consider all draectenn pairs simultaneously can introduce identity switches.
Graph Matching. This approach formulates data association as a graph problem, where vertices represent objects or tracklets and edges capture their relationships. GM [31 was the first to apply this method MOT, significantly improving association accuracy. GPM pioneers the abstraction of the multi-object tracking problem into frame-level point set matching. SuperGlue [42] combines graph matching with deep learning but mainly focuses on keypoint relationships, neglecting important intra-frame connections. GMTracker [32] enhances convergence speed by replacing the Sinkhorn layer with a graph matching network. However, graph matching methods have notable limitations: computational costs increase rapidly with the number of objects, and accuracy is highly reliant on the distinctiveness features, which restricts their scalability for large-scale MOT applications.
To address these challenges, we propose STAR, which utilizes spatial-temporal information to create distinctive and robust TC feature graphs, while efficiently reducing computational bottlenecks and maintaining accuracy through effective TC feature graph matching.

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Methodology
3.1 Problem Definition and Overview
Given an imput video sequence of L frames, the detected objects in frame are represented as where nt the number of objects. Each detection o, is tuple o, {pos;, f,, score, f.j. with pos; as the position, f, as the appearance feature, score; as the confidence score, and i as the timestamp. The goal of multi-object tracking (MOT) all objects by matching detections that refer to the same object, represented as 7 = {T1 ....... J. overview of STAR is shown Figure 2 The detection set is processed by the Confident Initial Tracklet Generator (CITG) to produce Confident Initial Tracklets (CITs), denoted as G important ensure that CITs are generated with strict object association strategy, each CTT corresponds to the same object. Due to occlusions, single object's trajectory may split into multiple CITs, leading to total number of CITs that typically greater than or equal to MOT is then reframed matching these CITs to produce the final object trajectories, denoted as {gi ,.,...,
I' {01,02, ....., On,
generate trajectories for
{g), 92, )
Unmatched Detections
Frame t-1
Frame
Tracklets 6 of individual objects
Dynamic loU-based association
Matched
91
Frame
Confident Initial Tracklet Generator
0.9
Propagation
F1
Frame t-1
TC,
Feature Graph ((M)
Graph Initialization (()
Tracklet Clip Graph Construction
Frume
Fa
Frame
Graph Matching
Matched Tracklets
Long-term Tracklets
Tracklet Clips Graph Matching
Figure 2: Overview of STAR. STAR consists of three essential components. CITG generates reliable initial tracklets
G. The TCGC produces TCs and constructs robust spatial-temporal feature graphs F(M), where each object TC represented as vertex, with black cuboids serving as empty placeholders in F.. TCGM facilitates efficient and accurate matching of object vertices across different TCs, ensuring robust associations.
3.2 Confident Initial Tracklet Generator (CITG)
A critical component of STAR is the CITG, which employs a strict association strategy. Tracklets are initialized based on object detections in the first frame and iteratively refined by calculating the IoU values between consecutive frames. If detection matches multiple tracklets, none are selected. By prioritizing detections with higher IoU values, generated tracklets correspond to same object across frames. A tracklet is considered terminated if it remains unmatched for three consecutive frames, ensuring that only reliable and consistent tracklets are retained. To enhance generality while maintaining reliable associations, the IoU threshold is dynamically adjusted based on factors such as

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object velocity, detection box size, and Initial Tracklets (CITs) provide a solid foundation for the subsequent stages TCGC and TCGM. Additional details can be found in Section A.2
time intervals. The reliably generated Confident
3.3 Tracklet Clip Graph Construction (TCGC)
Since CITs G {91, 92, ........... . 9Kk have been obtained, two tasks will then be performed: generating Tracklet Clips (TCs) and constructing the TC graph, as shown in Figure
TC
Propogation
TC
91
02
0
9x
Slicing
feature graph F
TC, feature graph (!M)
M x Propogation
Y verrtex Vvertex
V1
Yedge
Updated #2
Updated 12
V3
C2
2
VodgerVoertes! KAN Networks to fuse vertexes and edges features.
Figure 3: Overview of TCGC.
Generating Tracklet Clips. We arrange G by aligning in time and then them from time dimension, into fixed length Tracklet Clips (TC). Note that each TC contains trajectory segments the objects detected within that short time interval. For object that haven't 't appeared in frame in the TC,
an empty placeholder which is represent by black cuboid in Figure 3 We denote the generated TCs by TC (TC1, TC2,..., TCm), and the interval length of each TC is N consecutive frames. A TC contains at most objects. For each tracklet segment object, we apply weighted pooling to the position and appearance features based on confidence scores, resulting in the aggregated feature of = {posf, } for the (th object in the ith TC.
Constructing TC Graph. Each TC encodes the temporal features of the concurrently appeared objects during that interval. We then construct a graph for each TC, to further model the spatial, appearance, and topology features of the objects in TC. It involves two steps: initialization and propagation. Initialization builds a graph which treats the objects in the TC as vertices, and builds edges by distances and angles. Propagation refers the message-passing process that extracts high-order topological features. The appearance feature is also considered, and the two types of features concatenated. Since every TC is constructed using the same method, the superscript of TC is omitted. The constructed graph of each TC successfully encode the temporal, spatial and appearance features of the objects in the TC, making each object have more distinctive feature.
3.3.1 Initialization of a TC Graph
Appearance and topology features are utilized, which ensure stability under varying viewpoints. Due to dimensional differences between the topology and appearance features, divide the TC feature graph F = (V. E) into two parts: Fage and F Fugoo Each object is treated as a vertex. Let n, and n, represent the topology and appearance features of the kth vertex, and Cý and ěj.A represent the topology and appearance features of the edge (j, k) respectively. Finally, we combine these two feature graphs F F Fapp U Fmpoo where F Fispo (n, e) and F app (ñ.
Vertex topology features are constructed using normalized lengths, angles, and positions rela- tive to neighboring objects. We define the neighborhood set for vertex & as Ni. {9x | gt
5

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TC and dist(gy. 9*) S r x min wo) where r < 1 is constant, and ho and we are the height and width of the imput image, respectively. The function dist(-) represents the Euclidean distance between vertices. Then, the vertex topology features are as follows.
n (0)
htopo (pos,
|| [|1]105)
where
Jj Mj wi h
dist(gj 9k
pos, =
1,
gk EN
wo ho woo ho
max (ho, wo)
|| denotes concatenation, (ry, yy) are the bounding box center coordinates, and (w;, h2y are the bounding box dimensions. 8, represents the angle between adjacent neighborhood objects. We concatenate position, distance, and angle information and pass through Mopo a Kolmogorov- Arnold Network (KAN) [43] J. Edges are established between each vertex and its neighbors. The edge features are initialized based on positional and feature similarities.
(0) j.k
Stopo ([
Ijj 3k
ye ho
IOU (pos,, pos)) log
Wi wike
log
by hx
cos cos(E,, (.. fin)
fa)))
wi
(2)
The superscript (0) indicates the initial state. cos(-) denotes cosine similarity, and the appearance
features f E 12512 are obtained from Re-ID module [44]. The construction of Fapp follows
similar approach of Fropo: but using object's image appearance features. To balance the dimensions of topology and appearance features, we use three-layer KAN to reduce the dimension for vertex features in F app from 512 to 128.
3.3.2 Propagation
High-order features are obtained through iterative message passing. This process is executed for M iterations, with messages passed from vertices to edges and then from edges to vertices. The features of two connected vertices, n, and ng. first fused with the corresponding edge feature e₁, Then, each vertex ny aggregates messages from its neighboring edges and incident vertices.
(v + e) (12)
(m-1) + Vedge (nn
(m-1)
(m-1))
|| KAN (e j,k
())
j,k
= j,k
(e + v) : n (m) = 1/
(m-1) @nEEN /vertex (n) (m-
(m) J,k
n
Aggregating vertex features and neighboring edge features through three levels of fusion. (1) Fusion of Neighboring Vertex and Edge Features using the aggregation function Yvenex which employs KAN and ReLU activation function. Aggregation of Neighboring Features using a permutation- invariant and differentiable aggregation function @. (3) Final Vertex Feature Update using the
function "h The update step 77()) is based on the message normalization proposed in [45)
7) (n,, m KAN n; + ||n.lll
m;
|m,llp
where a learnable factor, and || ||2 denotes the L2 norm. This step ensures that updated vertex features remain well-scaled and balanced. The same propagation process is applied to F Fapp- resulting
in aggregated high-order features n! (M) and n i!(M) Finally, the vertex feature V/ the graph F is obtained by concatenating these features. This process ensures that the final vertex features encode rich spatial-temporal information, enabling robust associations in subsequent tasks.
3.4 Tracklet Clip Graph Matching (TCGM)
After each TC graph has been constructed. The next step is to perform matching between different TCs. Our approach TCGM involves inputting the existing graph (including both vertex and edge features) into a differentiable graph matching layer (32). which produces the matching output. The core component of Tracklet Clip Graph Matching (TCGM) the differentiable graph matching layer. This layer optimizes the matching process between detections and tracklets by solving Quadratic Programming (QP) problem
32 The result an optimal matching score vector æ, reshaped into matrix form X E N: xN to generate the matching score map. To ensure effective training, the gradients of the graph matching layer are computed using the KKT conditions, aided by the implicit function theorem [32]. TCGM leverages apatial-teppore information to establish

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robust associations between tracklets, significantly reducing identity switches caused by occlusions. By modeling interactions between tracklets within learnable and differentiable framework, TCGM improves the accuracy of the association stage, resulting in precise and reliable trajectory generation for MOT tasks.
Complexity Analysis. Previous graph matching methods construct graphs for individual objects in each frame, resulting in r'graph n X L &graphs, where is the number of objects and L is the number of frames. The complexity of graph matching algorithms typically ranges from Oriigaph) to ( graph log /graphh. By proposing TC graphs, we reduce the number of graphs to approximately NxE of the original count, remarkably improving the efficiency of graph matching.
2
3.5 Loss
To train the differentiable graph matching layer, we use a weighted binary cross-entropy loss function.
N1
N2
Luack
(N2 - 1) Us.k log (0g,) + (1 vs,x) log (1 (1- 1 85.x).
(5)
N1
where Új. is the predicted matching score between tracklet gj and tracklet ga. and Mj. is the ground truth indicating whether tracklet 9j belongs tracklet gk- (N2 - 1) is the weight used to balance the contributions of positive and negative samples to the loss. Due to the QP-based formulation of graph matching, the resulting score map tends have a relatively smooth distribution. To sharpen this distribution and improve the focus on high-confidence matches, we apply a softmax function with temperature T.
exp (IJ,A/T)
Dj.k- Softmax (Z),,
(6)
N2 ا exp (IJ,K/T)
where TjAk is the original matching score from the score map X, and T is the inverse temperature parameter. A smaller sharpens the distribution, while a larger smooths Moreover, the method applicable in both public and private detection settings. For public detection, the provided detections are directly used for tracking. For private detection, the training data is utilized to fine-tune the private detector. The total loss includes both detection and tracklet matching loss.
= Lastect + Lrackk
where Ldsteet denotes the object detection loss, which depends the private detector.
4 Experiments
4.1 Datasets and Metrics
Datasets. We select variety of challenging benchmarks. The MOTChallenge
46 47 datasets
feature diverse scenes, viewpoints, and weather conditions. MOT17 includes 14 videos (7 for training)
with three detection types: DPM (6]. Faster R-CNN [7]. and SDP (5) MOT20 focuses crowded scenes with 8 videos (4 for training and for testing) that employ Faster R-CNN 7 detections.
TrackTTrack (88 contains 100 videos of various group dances, while VisDrone2021-MO1 (49 comprises 96 sequences with around 40,000 frames across five object categories, posing challenges like occlusions and varying lighting conditions. These datasets present common issues in Multiple Object Tracking (MOT), such as frequent occlusions, irregular movements, and similar appearances, facilitating a comprehensive evaluation of STAR's robustness and generalization capabilities.
Metrics. The main metric for evaluating our method is the Higher Order Tracking Accuracy (HOTA) [50]. which provides balanced measure of both object detection accuracy (DetA) and association accuracy (AssA). Additionally, we also report MOTA (51). IDF1 [52), False Positives (FP), False Negatives (FN), Identity Switches (ID Sw.), and Frames Per Second (FPS) metrics.
4.2 Implementation Details
We employ several common data augmentation techniques, including random resize, crop, and color jitter. The input images are resized such that the shorter side is 800 pixels and the longer side is 1440
7

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pixels. The proposed method is implemented using Py Torch. During training, 2NN frames are sampled from each tracklet, resized 256 128 pixels, and divided into two N-frame clips enhance feature representation. The initial learning rate is set to 0.0003 and is reduced by factor of 0.1 every 40 epochs. The model is trained for 150 epochs using the Adam optimizer with mini-batch size of 32. Additional details and discussions can be found in Section A.4
4.3 Comparison with State-of-the-Art Methods
We compare STAR with numerous previous methods on the MOTChallenge
46 47 DanceTrack 48
and VisDrone2021-M8 -MOT 49 benchmarks, as shown in Table [
Table 2
Table 3 and Table
respectively. YOLOX S is used as the detector to ensure fair comparison. The best results are indicated in bold, and the second-best results are underline.
MOTChallenge. We evaluate STAR in the private detection setting and compare its performance against several state-of-the-ar algorithms. The results presented Table I and Table indicate that STAR consistently outperforms existing methods. Our tracklet-level paradigm effectively extracts and utilizes distinctive tracklet features, achieving improvements of 1.3% and 3.0% in HOTA on the MOT17 and MOT20 datasets, respectively, compared to SUSHI [53]
Table Performance comparison with state-of- the-art methods on the MOT17 [46 test set.
Table 2: Performance comparison with state-of-
Method
HOTA
MOTA
IDFI
Sw.
the-art methods on the MOT20 47 test set.
RaiMOT
73.7
GRTU [
62.0
74.9
75.0
1,812
Method
HOTA
MOTA
IDF1
ID Sw.
TLR [55
60.7
76.5
73.6
3,369
FairMOT 10]
54.6
61.8
67.3
5,243
MAA 115
62.0
79.4
1,452
MAA 115
57.3
73.9
71.2
1,331
GTR 56
59.1
75.3
71.5
2,859
ReMOT
63
61.2
77.4
73.1
1,789
MO3TR [77
60.3
77.6
72.9
2,847
ByteTruck 41
61.3
77.8
75.2
1,223
GMTracker 32]
ByteTrack
4T
64.9
63.1
80.3
80.6
79.8
2,196
1.197
GMTrucker
32
62.9
77.8
76.7
1,331
BPM
62.3
1,314
FeatureSORT
58
64.2
80.6
76.7
2,637
BASE [6
63.5
78.2
77.6
984
OmniTrack
13
62.3
79.1
75.6
1,968
Sparse
60
63.4
78.1
77.6
1,120
BPMTrack
59
63.6
81.3
78.1
2,010
Boos|Track++
66.4
77.7
82.0
762
SparseTTrick
OccluTrack=
62
66.7
82.7
429
BoostTruck++ 61]
66.6
80.7
82.2
1,062
SUSHI
64.3
74.3
79.8
706
OccluTrrack
62
66.8
80.2
82.8
951
STAR
67.3
79.6
79.0
1,047
SUSHI
66.5
81.1
83.1
1,149
STAR
67.8
81.9
80.2
1.057
TrackTrack. The complex scenarios characterized by frequent occlusions and irregular motion present significant challenges for tracking systems. Using the same pre- pre-trained detector, STAR shows substantial improvements over Sparse Track [60], with improvement of 0.4% in HOTA, 0.5% in MOTA, 0.4% in AssA, and 0.2% in DetA as indicated in Table 3
Table 3: Performance comparison with state-of- thae--t meth- ods on the DanceTrack [8 test set.
Method
HOTA
MOTA
IDF1
AssA
DetA
CenterTrack
19
41.8
86.8
35.7
22.6
78,1
TraDes [22
43.3
86.2
41.2
25.4
74.5
OCSORT 33
55.1
92.0
54.6
38.3
80.3
FairMOT
10
39.7
82.2
40.8
23.8
66.7
QDTrack 64
54.2
87.7
50.4
36.8
80.1
GTR [56
48.0
84.7
50.3
31.9
72.5
Brackrack
41
47.7
89.6
53.9
32.1
71.0
BoT-SORT 11
54.7
91.3
56.0
37.8
79.6
Sparse
60
55.5
91.3
58.3
78.9
STAR
55,9
91.8
57.9
39.5
79.1

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These results highlight the considerable potential of our method in managing occlusion scenarios. Notably, even with simple IoU distance association strategy, STAR achieves comparable or even superior performance relative to other methods.
greater challenges due frequent occlusions and varying lighting conditions. As shown in Table 4 STAR surpasses BoT-SORT by 2.9% in HOTA and 1.4% IDFI, while outperforming OCSORT [33 by 2.4% in HOTA and 6.2% in IDFI. Furthermore, STAR demonstrates a 0.4% improvement in IDFI over UGT while achieving a 1.6 FPS advantage, highlighting its efficiency in aerial tracking tasks.
VisDrome2021-MOT. This dataset presents
Table 4: Performance comparison with state-of-the-art methods on the set.
ViSDronc2021-MOT 49
Method
HOTA
MOTA
IDFT
FN
FP
II) Sw.
FPS
DeepSORT
65
36.9
34.4
46.7
110,989
21,077
1,784
18.5
ByteTrrck
40.7
39.5
50.4
105,518
16,257
1,581
31,2
BoT-SORT
42.4
41.7
56.8
108,505
14.114
1,430
25.3
66
40.2
40.7
48.8
103,188
15,794
2,029
28.5
MOTDI 67
37.0
35.5
52.6
106,006
15,385
2,668
26.2
OCSORT
$2.0
132,279
22,019
2,859
StrongSORT 35
36,6
33.3
42.5
185,503
12.214
1,980
12.1
UCMCTTrack
[4]
37.1
28.1
38.4
150,590
9,244
7,231
17.8
QDTrack
44.7
39.1
55.3
104,759
34,242
2,627
10.3
OU'Track 68
34.0
35.0
44.5
115,570
25,276
3,335
17.4
FairMOT 10
31.1
12.8
37.7
114,834
59,997
3,072
21.5
TrackFormer
69
35.3
25.0
51.0
141,526
25,856
1,534
7.0
SGT 24
43.6
54.8
110,652
7,707
951
UGT 25
45.5
41.8
57.8
101,074
15,174
618
16.2
STAR
45.3
41.9
58.2
100,832
15,289
602
17.8
These results emphasize STAR's strong tracking stability and superior performance. effective tracklet feature and the matching of TC graph enable STAR to
modeling of distinctive achieve state-of-the-ar results on MOTChallenge, DanceTracks and VisDrone202I-MOT, establishing new benchmark for multi-object tracking (MOT).
4.4
Ablation analysis
4.4.1 Effect of Tracklet Clip Graph Construction (TCGC)
Each TC consists of N consecutive frames. The analysis of the frame count (N) per TC is shown in Table and Figure 4 can be observed that N 6 performs the best and N 4 the second-best. Although there was slight improvement at N = 6, it was not significant. Therefore, we opted for ensure robust performance challenging scenes.
N
For the sampling strategy with various stride lengths assess their impact model performance. The detailed experimental results are presented Table The model achieved optimal performance at a stride length of L = This smaller stride length allowed for more effective capture of continuity and changes over time, thus enhancing the model's temporal data processing capabilities. Furthermore, comparing stride lengths of L. 2 and I. with window size of N 4, found that although both settings yielded good performance. the L = 2 configuration provided high accuracy and improved efficiency.
4 and a stride length L in TCGC, we experimented with
Appearance similarity serves as baseline for tracklet association. As shown in Table 7 distinctive and robust tracklet features significantly enhance tracking performance compared to models that rely solely IoU distance for association. While Global Average Pooling (GAP) utilizes simpler pooling strategy, our TCGC improves IDF1 from 61.9% to 69.2%. These results clearly highlight the importance of advanced tracklet feature extraction.
4.4.2 Effect of STAR
We demonstrate that integrating our proposed STAR method with existing tracking approaches enhances their performance, as summarized Table & The evaluated baseline methods, Deep- SORT 655 JDE [70), and CTracker (71), utilize IoU distance and frame-level features for tracklet

---

Table 5: Performance evaluation for various N values.
100
mAP
Top-1
Top-5
Top-10
Score(%)
94
N=1
2
91.14
89.52
98.89
98.89
92.52
91.84
98.89
98.89
92
X=
90
92.55
91.92
98.89
98.89
88
92.60
91.95
98.89
98.89
MAP
Top1
Tops
Top10
92,43
91.36
98.89
98.89
Metries
92.04
90.88
98.89
98.89
Figure 4: Effect of in TCGC.
Table 6: Experimental results for various combi- Table 7: Effect of Different Tracklet Description Methods.
nations of window size N and stride length L..
N
L
HOTA
MOTA
IDF1
ID Sw.
Methods
IDFI
HOTA
MOTA
ID Sw.
4
1
67.9
82.1
80.4
1,023
Baseline
61.9
55.5
57.6
296
4
2
67.8
81.9
80.2
1,057
GAP
63.1
55.6
57.6
209
4
3
67.4
81.5
79.6
1,129
STTA 20
68.6
58.7
57.7
133
4
67.0
81.2
79.5
1,327
TCGC
69.2
59.0
57.9
125
generation, with CTracker also incorporating topological information for data association. When com- paring the baseline versions with their modified counterparts (DeepSORT* JDE*, and CTracker*)) that rely solely on loU distance, we observe comparable MOTA scores but significant drop in IDF1 (from 62.0% 55.0%). This decline highlights IoU distance limitations in maintaining identity con- sistency across long trajectories and emphasizes the necessity for advanced feature extraction and data association techniques. Integrating STAR with these methods boosts performance, improving IDFI by 3.2%, 3.5%, and 8.0% for DeepSORT, JDE, and CTracker, respectively. These results demonstrate STAR's 's effectiveness in enhancing identity preservation and association accuracy. conclusion, STAR is model-ague aggestig and can be seamlessly integrated into various tracking frameworks.
Table 8: Performance of Adding STAR upon Existing Methods on the MOT16 Training Dataset.
Methods
MOTA
IDF1
HOTA
FP
EN
Sw.
Decpsort
56.9
62.0
51.3
13,227
33,454
932
Deepsort*
56.6
55.0
48.3
10,433
35,627
1.883
Decpsont*
58.8
65.2
54.3
13.041
31,105
1.315
JDE 770
73.1
68.9
55.1
6,593
21,788
1.312
JDE
73.0
61.9
53.6
6,185
22,296
1.330
+SARR
73.5
72.4
55.3
6,871
21,350
1,125
CTrakeer
76.2
68.6
61.1
2,149
23,188
912
CTrakke**
76.2
68.6
61.1
2,149
23,188
912
Crracker* SSARR
78,8
76.6
66.0
3,981
18,960
$40
Limitation and Conclusion
Unlike previous approaches that overlook historical information and temporal continuity, STAR ex- tracts and effective leverages distinctive tracklet features through TC to address occlusion challenges. The framework consists of three core components. CITG efficiently generates reliable CITs, TCGC produces discriminative TC feature graphs by exploring and TCGM uses graph matching enhance association accuracy and improve efficiency. Together, these components produce high-integrity trajectories and achieve state-of- ofte-oo-thearrr art performance across three widely used benchmarks, demonstrating STAR's effectiveness and robustness. Despite its strong performance, the method faces efficiency limitations. Future work will focus on designing an end-to-end tracking framework to further enhance STAR's 's robustness and applicability in real- cal-world multi-object tracking (MOT) scenarios.
information within tracklets,
10

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14

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A
Appendix
A.1 Overview
In the supplementary material, primarily:
1. Provide more details of CITG in Appendix A.2
2. Provide overview of TCGM in Appendix A.3
3. State more experimental details in Appendix A.4
4. Provide additional experimental results in Appendix
A.S
A.2
Construction of CITG
A short-term trajectory continues be associated with the detection in the next frame until it no longer matches any detection. The Confident Initial Tracklet Generator utilizes the Intersection over Union (IoU) metric to associate objects by calculating the overlap between detection boxes. Below is the detailed process.
Trajectory Initialization: In the first frame of the video, each detection box is initialized as an independent short-term trajectory.
For each trajectory in the current frame, the IoU with all detection boxes in the subsequent frame is calculated. The loU value, ranging from to indicates the degree of overlap, with higher values denoting greater overlap.
IoU Calculation:
Trajectory Matching:
Trajectories are matched detection boxes based loU values. A trajectory and a detection box from the next frame are considered to belong to the same object their loU exceeds specific threshold. If detection box matches multiple trajectories, none selected. Unmatched detection boxes are initialized as new trajectories.
Dynamic Adjustment of ToU Threshold: To accommodate varying motion characteristics of targets, a dynamic adjustment method for the loU threshold is employed. This method considers multiple factors, including the target's s motion speed (v), detection box size (A), and time interval between frames (At). These factors dynamically influence the loU threshold.
Trajectory Update and Termination: Matched detection boxes are added to corresponding trajec- tories, updating their state (such as location and timestamp). Trajectories that remain unmatched for more than three consecutive frames are terminated. Unmatched detection boxes initiate new trajectories.
Output Short- Term Trajectories: The aforementioned steps generate a set of short-term trajectories that capture the preliminary motion trajectories of all targets in the video. These trajectories form the basis for further trajectory association and long-term trajectory generation.
A.3 Overview of TCGM
A.4 More Implementation Details
We train our model on 24 NVIDIA RTX 2080Ti GPUs. During testing, the entire tracklet is used as input, with every N=4 frames treated as a clip.
MOTChallenge. the MOTI6 dataset [46], only objects with a visible ratio greater than 0.3 are selected, resulting in 517 training identities, 438 gallery identities, and 429 query identities. The total number of training videos 2,065, and there are 2,173 testing videos, with each ground truth trajectory divided into four variable-lenge tracklets.
VisDrone2021- MOO. The VisDrone2011-MTTrree set, which consists of 56 sequences, used for training, while the VisDrone2021 1etee2021-OOT-ER -MOT-test-dev set, containing 17 sequences, is used for testing. During evaluation, single tracklet per identity serves as the query, with the remaining tracklets in the gallery.
15

---

V
113
Tracklet Clip feature graph
Graph Maching Network
e
F1,4
Tracklet Clip feature graph
Figure 5: Overview of TCGM.
UAVDT. STAR is also evaluated on the UAVDT dataset [72]. which provides diverse scenarios and challenges. The UAVDT dataset contains 50 sequences with over 80,000 frames, capturing various situations that present frequent occlusions and significant camera motion.
A.5 More Comparisons with State-of-the-Art Methods
We compare the proposed STAR method with traditional TBD approaches using Tracktor to refine detections the public detection setting. As shown Table STAR outperforms existing methods on most evaluation metrics, achieving significant improvements in MOTA, IDF1, and HOTA across the MOTI5, MOT16, MOT17, and MOT20 datasets. The tracklet-level approach of STAR integrates tracklet information during feature extraction and data association, which facilitates more robust tracking.
Table 9: Performance comparison of Public Detection with state-of-the the-th--trerrrr art methods on the MOTChal- lenge test set. The best results are indicated in bold, and the second- best results in underline.
Datasets
Methods
HOTA
MOTA
IDF1
FP
ID Sw.
Trucktor 76
37.6
46.6
47.6
4,624
26,896
1,280
MOT15
LFP
74
43.8
47.2
57.6
7,635
24,277
554
MPNTrack
75
45.0
51.5
58.6
7,620
21,780
375
STAR
45.8
55.8
59.9
5,983
20,700
475
Tracktor
78
44.6
56.2
$4.9
2,394
76,844
617
GSM 22
45.9
57.0
$8.2
4,332
73.543
475
LFP (14
49.6
57.5
64.1
4,249
72,868
335
MPNTrack
48.9
58.6
61.7
4,949
70.252
354
MOT16
LPC 76
51.7
58.8
67.6
6,167
68.432
435
MO3TR 57
50.3
64.2
60.6
7,620
56,761
929
TMOH
50.7
63.2
63.5
3,122
63.3776
GMTracker
30
48.9
55.9
63.9
2,371
77,545
531
STAR
$1.2
64.1
66.5
2,427
62,377
511
Tracktor
73
44.8
56.3
$5.1
8,866
235,449
1,987
22
45.7
56.4
14,379
230,174
1,485
LFP 74
50.7
58.2
65.2
16,850
217.944
1,022
MPNTrack
25
49.0
58.8
61.7
17,413
213,594
185
MOT17
LPC 76
51.5
59.0
66.8
102
206,948
1,122
MOTTR 57
49.6
63.2
60.2
21,966
182,860
2,841
TMOH
77
50.4
62.1
62.8
10,951
201,135
1,897
GiTracker
49.1
56.2
63.8
8,719
236,541
1,778
STAR
52.5
64.2
66.5
8,971
190,636
1,994
Tracktor
73
42.1
52.6
52.7
6,930
236,680
1,648
MOT20
TMOH
K2
48.9
60.1
61.2
8,043
165,899
2,342
STAR
52.9
64.1
66.5
39,357
143,583
2,825
16

---

For the UAVDT dataset, 40 sequences are randomly selected for training, while 10 sequences are designated for testing. As summarized in Table TO STAR demonstrates superior performance on the UAVDT dataset, achieving HOTA, MOTA, and IDFI scores of 63.4%, 71 71.4%, and 80.3%, respectively, surpassing other methods. STAR outperforms BoT-SORT [1]] by 2.0% HOTA and 3.7% in MOTA, highlighting its effective modeling of topological relationships. Additionally, STAR exceeds UGT [25], the pessforeroriing method on UAVDT, by 1.4 FPS in terms computational efficiency.
Table 10: Performance comparison with state-of-the-ae methods on the UAVDT best results are indicated in bold, and the second-best results are in underline.
72
test set. The
Method
НОтA
MOTA
IDF1
FN
FP
ID Sw.
FPS
DeepSORT
62.0
68.5
78.6
20,035
4,008
61
20.3
ByteTrack
62.2
20,010
3,796
102
BoT-SORT 0
61.4
67.7
78.5
20.296
4,323
64
25.0
Tracker 66
62.9
70.3
79.6
17,405
5,224
79
38.1
MOTDT 667
61.8
66.5
77.8
17,760
5,824
76
22.4
OCSORT 33
59.8
69.5
74.5
18,246
6,480
249
13.3
StrongSORT 35
58.3
49.3
72.6
10,606
28,032
134
10.6
UCMCTTrack
14
54.1
61.0
65.9
25,888
2,482
984
15.4
QDTrack
64
61.2
70.3
76.1
17,304
6,420
92
13.4
OUTrrakk
68
58.6
64.3
66.5
18,193
7,826
240
18.2
FairMOT
10
49.1
51.2
66.5
33,102
4,136
110
25.5
TrackFormer
43.2
37.9
45.197
5,582
680
7,94
SGT
24
58.2
77.0
11,658
20,598
102
30.0
UGT
63.6
71.6
80.0
17,285
4,357
67
18.3
STAR
63.4
71.4
80.3
17,462
4,389
74
19.7
17

---

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Reviews:
[{"review_id": "eAXZuKdsgm", "reviewer": "Reviewer_pCN3", "summary": "This paper proposes STAR, an association module based on tracklet clips. It initializes sequences of multiple target trajectories through IoU between detection boxes. Subsequently, a Tracklet Clip Graph is constructed on the temporal sequence, followed by graph matching, which ensures spatio-temporal consistency of the matches. STAR is a plug-and-play module that can be seamlessly integrated into various downstream models. It demonstrates significant performance improvements when compared to baselines.", "questions": "1.\tIf time permits, the authors could analyze whether there should be some degree of overlap or skipped-frame sampling for the frame sampling of Slicing TCs. This is because overlap can provide implicit temporal feature propagation, which can implicitly extend the length of the temporal sequence under consideration.\n2.\tDo the authors have more data to explain the impact of the number of TCs (number of frames retained) and the number of frames retained in the expanded graph on experimental performance?\nIf the authors address the above questions, I would consider increasing the score.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1.\tThis paper explores a plug-and-play long-range association module, which holds significant value for downstream applications.\n2.\tThe unified association paradigm based on graph matching designed in this paper achieves unified spatio-temporal matching, avoiding the matching ambiguity issues caused by traditional one-by-one matching. Instead, it considers matching from the perspective of the entire trajectory sequence, which brings new insights to the field.\n3.\tThe paper is well-written, with clear descriptions and logical flow.\nWeakness:\n1.\tThe paper uses too many abbreviations, and they are quite similar, which hinders continuous reading for the reader. The authors might consider using more distinctive names or not over-abbreviating certain algorithms, simply referring to them as \"Graph Matching\" would suffice.\n2.\tThe paper unfolds the graph in a temporal sequence to achieve matching. However, how are cases handled where multiple TC (Tracklet Clip) matching results are inconsistent? For example, if ID=1 and ID=2 frequently switch IDs within a short period, would this pose a greater challenge for subsequent matching?\n3.\tThe paper claims to achieve long-range object tracking. However, the ablation study shows that N=4 yields the best results, with performance still improving compared to N=8. I personally believe this length (even 2N=8 frames) is not particularly long.\n4.\tThe authors did not analyze how much impact the proposed module has on inference performance.", "was_revised": true, "final_justification": "The rebuttal has made more analyses on the key modules of the proposed method. And my concerns have been resolved.", "rebuttal": "Dear Reviewer pCN3,\n\nThank you for your detailed review and invaluable comments. We will continuously refine our expressions and abbreviations to further enhance readability. We address your concerns point by point and welcome any follow-up questions for clarifications.\n\n---\n\n**Q1:** If time permits, the authors could analyze whether there should be some degree of overlap or skipped-frame sampling for the frame sampling of Slicing TCs. This is because overlap can provide implicit temporal feature propagation, which can implicitly extend the length of the temporal sequence under consideration.\n\n**Response to Q1:**\nWe deeply appreciate the reviewer's scrutiny and suggestions, which are crucial for enhancing our work. Regarding your recommendations on overlapping and skipped-frame sampling strategies, we conducted an in-depth study to explore the best possible configurations.\n\nPreviously, we adopted a sampling strategy with a sliding window size of N=4 and a stride length of L=2. Based on your advice, we experimented with various combinations of window sizes and stride lengths to evaluate changes in model performance. The specific experimental results are as follows.\n\n| N | L | HOTA | MOTA | IDF1 | ID Sw. |\n|---|---|------|------|------|--------|\n| 4 | 1 | 67.9 | **82.1** | 80.4 | **1,023**  |\n| 4 | 2 | 67.8 | 81.9 | 80.2 | 1,057  |\n| 4 | 3 | 67.4 | 81.5 | 79.6 | 1,129  |\n| 4 | 4 | 67.0 | 81.2 | 79.5 | 1,327  |\n| 6 | 1 | **68.0** | 82.0 | **80.5** | 1,042  |\n| 6 | 2 | 67.7 | 81.7 | 80.2 | 1,038  |\n| 6 | 3 | 67.9 | 81.6 | 79.9 | 1,085  |\n| 6 | 4 | 67.1 | 81.1 | 79.6 | 1,121  |\n| 6 | 5 | 66.8 | 80.8 | 79.3 | 1,258  |\n| 6 | 6 | 66.5 | 80.5 | 79.0 | 1,275  |\n\nWhen the stride length is L=1, regardless of whether the window size is N=4 or N=6, the model performance was optimal. The smaller stride length, L=1, allowed us to more effectively capture continuity and changes over time, thus enhancing the model's ability to process temporal data.\n\nSimultaneously, we compared the effects of stride lengths L=2 and L=1 with a window size of N=4 and found, although both configurations performed well, the L=1 setup allowed the model to maintain high accuracy while improving efficiency.\n\n---\n\n**Q2:** Do the authors have more data to explain the impact of the number of TCs (number of frames retained) and the number of frames retained in the expanded graph on experimental performance?\n\n**Response  to Q2:** \nThank you for your detailed feedback. We understand your concerns about the tracking length. After evaluating performances for N=4, 5, 6, 7, 8, which is shown in the table below. Although there was a slight improvement at N=6, it was not significant. Therefore, we opted for N=4 to ensure robust performance in challenging scenes.\n\nWe recognize that N=4 may not seem to cover a long-range, but based on our research, this duration is already ahead of many existing studies where tracking usually does not exceed 4 frames. As such, even 4-frame tracking represents an advancement.\n\n| N | mAP   | Top-1 | Top-5 | Top-10 |\n|---|-------|-------|-------|--------|\n| 2 | 91.14 | 89.52 | 98.89 | 98.89  |\n| 4 | 92.52 | 91.84 | 98.89 | 98.89  |\n| 5 | 92.55 | 91.92 | 98.89 | 98.89  |\n| 6 | **92.60** |**91.95** | 98.89 | 98.89  |\n| 7 | 92.43 | 91.36 | 98.89 | 98.89  |\n| 8 | 92.04 | 90.88 | 98.89 | 98.89  |\n\n---\n\n**Q3:** The paper unfolds the graph in a temporal sequence to achieve matching. However, how are cases handled where multiple TC (Tracklet Clip) matching results are inconsistent?\n\n**Response to Q3:**\nThank you for your question, it addresses a core aspect of our method. To handle frequent ID switches, our system sets strict criteria in the \"Confident Initial Tracklet Generator\" stage, where tracklet clips must meet rigorous conditions to be deemed \"confident\". Instances where IDs such as 1 and 2 switch frequently are not classified as confident, ensuring that unstable or low-confidence segments don't adversely influence the matching process. This approach enhances the robustness of our method. Our experiments on the Dancetrack dataset validate its effectiveness even amidst frequent ID switches.\n\n---\n\n**Q4:** The authors did not analyze how much impact the proposed module has on inference performance.\n\n**Response to Q4:**\nThank you for your valuable comments. We fully understand your concern about how the proposed module impacts inference performance. Indeed, the graph matching is a quadratic programming problem which could affect speed. To mitigate this, we introduced the Gated Search Tree algorithm in inference stage, which enhances efficiency through these three key steps:\n\n1. Bipartite Graph Construction: We construct bipartite graphs between trajectory segments, establishing connections only under specific appearance and motion conditions.\n\n2. Connected Components Identification: We use depth-first search to reduce the scale of matching computations.\n\n3. Parallel Computation of Matching Costs: We parallelize the computation of matching costs across connected components, using a streamlined optimization approach for rapid processing.\n\nThe algorithm significantly accelerates inference speed through parallel processing while maintaining accuracy. We hope these explanations address your concerns, and we appreciate your thorough review of our research. We look forward to your further feedback."}, {"review_id": "QyGNW3Kgmk", "reviewer": "Reviewer_MVNd", "summary": "This paper proposes STAR, a novel multi-object tracking framework that reframes the object association problem by operating at the tracklet clip level rather than individual detection instances. STAR constructs spatial-temporal tracklet clip graphs incorporating appearance, motion, and topological features, and performs graph matching to reliably associate object tracklets over time. The framework is designed to handle frequent occlusions, viewpoint changes, and crowded scenarios. It is model-agnostic and can be integrated into existing MOT systems. Evaluations show that STAR achieves state-of-the-art performance across multiple metrics.", "questions": "1. Section 3.2 is quite brief and lacks details; for instance, how is the IoU threshold dynamically adjusted based upon the mentioned factors (object velocity, detection box size, and inter-frame time intervals)?\n1. Has there been a study conducted to verify that the CITG makes \"tracklet initialization more robust across diverse tracking scenarios\" (L130)?", "limitations": "yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n1. The paper addresses a persistent and well-known challenge in multi-object tracking (MOT): maintaining reliable object associations under frequent occlusions and viewpoint changes.\n1. The STAR framework introduces a tracklet clip graph matching paradigm, which is a novel reframing of the object association problem that directly addresses limitations in current MOT paradigms.\n1. Clear, well-motivated problem statement targeting known limitations in existing MOT methods.\n1. Technically novel approach leveraging tracklet clip graph matching for more robust, long-term association.\n1. Strong experimental validation across multiple public benchmarks, consistently outperforming existing state-of-the-art methods.\n1. Thorough ablation and complexity analysis, demonstrating the contribution of each module and computational benefits.\n1. Model-agnostic integration, showing measurable improvements when paired with various baseline tracking systems.\n\n## Weaknesses\n1. While the complexity analysis shows significant improvement over conventional graph matching, real-time deployment at large scale is still constrained.\n1. While the paper provides extensive quantitative results and ablation studies, it lacks a qualitative or case-specific analysis of where and why STAR may still fail (e.g., in highly dynamic or extremely long occlusion cases).\n1. No run-time performance benchmarking: the paper discusses computational complexity reduction at the graph matching stage and reports FPS values, but it’s unclear how STAR would perform in latency-sensitive deployments like autonomous driving or surveillance on edge devices.\n1. No analysis on domain generalization: there’s no experiment on how STAR handles domain shift (e.g. indoor to outdoor, day to night).", "was_revised": true, "final_justification": "This paper merits acceptance for its novel and well-motivated approach to a long-standing challenge in multi-object tracking: maintaining reliable associations under occlusion and viewpoint changes. The STAR framework introduces a tracklet clip graph matching paradigm that reframes the association problem, with strong empirical results across benchmarks. The authors clarified key implementation details in their rebuttal—especially the dynamic IoU thresholding in the Confident Initial Tracklet Generator—and added a compelling ablation study showing clear performance gains over multiple baselines. STAR is model-agnostic, computationally efficient, and improves tracking performance consistently. While real-time deployment and domain generalization remain open issues, the paper’s contributions justify its acceptance.", "rebuttal": "Dear Reviewer MVNd,\n\nThank you for the detailed review and invaluable comments. We address your concerns point by point and invite you to ask any follow-up questions if anything remains unclear.\n\n---\n\n**Q1:** Section 3.2 is quite brief and lacks details; for instance, how is the IoU threshold dynamically adjusted based upon the mentioned factors (object velocity, detection box size, and inter-frame time intervals)?\n\n**Response to  Q1:** A short-term trajectory $g_k= \\{o_{1}^{k},o_{2}^{k},\\cdots,o_{n_k}^{k}\\}$ continues to be associated with the detection in the next frame until it no longer matches any detection. The Confident Initial Tracklet Generator utilizes IoU (Intersection over Union) to achieve the association of objects by calculating the overlapping extent between detection boxes. Here is the specific process:\n\n1. **Trajectory Initialization**\n \n   In the first frame of the video, each detection box is initialized as an independent short-term trajectory.\n\n2. **IOU Calculation**\n\n   For each trajectory in the current frame, the IoU value with all detection boxes in the next frame is calculated. The IoU value represents the overlapping extent between two detection boxes, ranging from $[0, 1]$, where a higher value indicates a greater degree of overlap.\n\n3. **Trajectory Matching**\n\n   Matching of trajectories and detection boxes is based on the IoU values: if the IoU value of the last detection box of a trajectory with a detection box in the next frame exceeds a certain threshold, they are considered to belong to the same object. If a detection box matches multiple trajectories, none are selected. Unmatched detection boxes are initialized as new trajectories.\n\n4. **Dynamic Adjustment of IOU Threshold**\n\n   To accommodate the motion characteristics of targets, a dynamic IoU threshold adjustment method based on multiple factors is adopted. Specifically, the IoU threshold is dynamically adjusted considering the target's motion speed, detection box size, and the time interval between frames.\n \n   $$\\tau = \\left( \\tau_{0} - \\alpha \\cdot v + \\beta \\cdot \\log(A) + \\gamma \\cdot \\Delta t \\right)$$\n   $$\\tau = \\max(\\tau_{\\text{min}}, \\min(\\tau_{\\text{max}}, \\tau))$$\n\n   Here, \\( v \\) is the object's motion speed (calculated using the Euclidean distance between center points across frames), \\( A \\) is the detection box area, and \\( \\Delta t \\) is the time interval between frames. The motion speed, detection box size, and time interval dynamically influence the IoU threshold.\n\n5. **Trajectory Update and Termination**\n\n   Matched detection boxes are added to corresponding trajectories, updating the trajectory's state (such as location and timestamp). Trajectories that remain unmatched for multiple consecutive frames (more than 3 frames) are considered terminated. Unmatched detection boxes initiate new trajectories.\n\n6. **Output Short-Term Trajectories**\n\n   These steps generate a set of short-term trajectories containing the preliminary motion trajectories of all targets in the video, serving as the foundation for subsequent trajectory association and long-term trajectory generation.\n---\n\n**Q2:** Has there been a study conducted to verify that the CITG makes \"tracklet initialization more robust across diverse tracking scenarios\" (L130)?\n\n**Response to Q2:** Thank you for emphasizing the importance of the ablation study on the CITG.\nWe realize the significance of this component within our tracking framework. \nWe have now conducted a detailed ablation study to evaluate the role of the IoU-based Association. \nDeepSORT*, JDE*, and CTracker* represent modified DeepSORT, JDE, and CTracker, which rely solely on IoU distance.\nResults from this study show that CITG perform best. We appreciate your suggestion and believe that this addition strengthens our paper significantly.\n\n| **Methods** | **MOTA** | **IDF1** | **HOTA** | **ID Sw.** |\n|-------------|----------|----------|----------|------------|\n| Deepsort*+STAR | 58.8 | 65.2 | 54.3 | 1,315 |\n| JDE*+STAR | 73.5 | 72.4 | 55.3 | 1,125 |\n| CTracker*+STAR | 78.8 | 76.6 | 66.0 | 540 |\n| CITG+STAR | **80.6** | **77.2** | **67.5** | **326** |\n\nThis study underscores the robustness of CITG in diverse scenarios.\n\n---\n\n**Q3:** While the paper provides extensive quantitative results and ablation studies, it lacks a qualitative or case-specific analysis of where and why STAR may still fail (e.g., in highly dynamic or extremely long occlusion cases).\n\n**Response to Q3:** STAR failure in two typical  cases. The first case involves a small-scale pedestrian who is occluded for an extended period while being captured by a rapidly moving camera. In this scenario, the combination of the pedestrian's small size and the camera's fast motion can lead to significant deviations in the pedestrian's trajectory. To address this problem, it is recommended to utilize more advanced technologies, such as optical flow, which can provide improved motion compensation for the fast-moving camera. \n\nThe second failure case arises from false detections produced by the object detector. For instance, when the detector erroneously identifies a non-existent ID, the tracking algorithm incorrectly assigns a track to this false detection. To tackle this challenge, it is vital to develop more robust object detectors or to refine our approach into an integrated detection and tracking method, which can significantly reduce such errors.\n\n---\n\nWe hope these explanations address your queries effectively. Thank you for your attention to our work, and we look forward to your further questions or suggestions."}, {"review_id": "y0hwOrnPFT", "reviewer": "Reviewer_pdkw", "summary": "The paper introduces a Spatial-Temporal Tracklet Graph Matching method for multiple object tracking. To address occlusion issues, trajectory graphs are built by combining tracklet clips and graph matching. However, there are some concerns regarding motivation and performance.", "questions": "1. Clarify how the proposed methods address the conclusion issues\n2. Include more recent state-of-the-art works\n3. Provide the ablation study on the IoU-based Association", "limitations": "No, how the proposed methods address the conclusion issues.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n* The evaluation is comprehensive and conducted on several datasets.\n* The presentation is clear and easy to follow.\n\nWeaknesses\n* The Confident Initial Tracklet Generator heavily relies on the Dynamic IoU-based association. However, IoU-based Associations are not strictly reliable and generate wrong tracklets, especially after occlusion. Consequently, the constructed graph can be polluted, which weakens the purpose of the graph construction. Meanwhile, the ablation study should also show the results where the tracker solely relies on the IoU-based association and the effects of the association (using association results and GTs on the MOT-17 validation set).\n* The STAR aims to address the occlusion issues. However, the methods do not provide information or explanations indicating how each implementation contributes to the motivation to address the occurrence.\n* In the literature review and benchmarks, some state-of-the-art methods are missing, such as BoosTrack++ and OccluTrack. Meanwhile, the performance on the MOT 17 and MOT 20 datasets is not comparable with some of those methods. In addition, HOTA should be included in the benchmark, which is the most important metric for evaluating tracking performance.\n1. Stanojević, V., & Todorović, B. (2024). BoostTrack++: using tracklet information to detect more objects in multiple object tracking. arXiv preprint arXiv:2408.13003.\n2. Gao, J., Wang, Y., Yap, K. H., Garg, K., & Han, B. S. (2025). Occlutrack: Rethinking awareness of occlusion for enhancing multiple pedestrian tracking. IEEE Transactions on Intelligent Transportation Systems.", "was_revised": true, "final_justification": "The comparison is comprehensive and improved, and the proposed method shows some novelty. I increased the rating.", "rebuttal": "Dear Reviewer pdkw,\n\nThank you for the detailed review and invaluable comments. We address your concerns point by point and invite you to ask any follow-up questions if anything remains unclear.\n\n---\n\n**Q1:** The STAR aims to address the occlusion issues. However, the methods do not provide information or explanations indicating how each implementation contributes to the motivation to address the occurrence.\n\n**Response to Q1:**\n\nThank you for your detailed review. With respect to the mechanism of STAR in managing occlusions, we have enhanced feature discernibility in occluded scenarios by integrating both temporal information and topological relationships into our system. Let me further clarify the application of these methods:\n\n**Temporal Information**: By generating tracklets and extracting features over time, STAR enables the system to infer the characteristics of a object even when direct observation is momentarily obstructed. For instance, in a dense crowd, should an individual be temporarily hidden by others, the system continues to project their likely whereabouts until they become visible again.\n\n**Topological Relationships**: The spatial interrelations among objects serve as another robust strategy to address occlusions. In scenarios like vehicle tracking, the relative positions of multiple objects typically exhibit short-term consistency. Leveraging this attribute, the STAR system preserves the integrity of the topological structure among objects, thereby facilitating the recognition and tracking of occluded objects.\n\nTo validate the efficacy of these approaches, rigorous testing was conducted using the dancetrack dataset, known for its frequent occlusions. The outcomes affirm that the fusion of temporal and topological information elevates the HOTA to 55.9\\%. This underscores the substantial improvement in handling occlusions, attesting to the robustness of our method.\n\n---\n\n**Q2:** In the literature review and benchmarks, some state-of-the-art methods are missing, such as BoosTrack++ and OccluTrack. Moreover, the performance on the MOT 17 and MOT 20 datasets is not comparable with some of those methods. Additionally, HOTA should be included in the benchmark, which is the most important metric for evaluating tracking performance.\n\n**Response to Q2:**\nWe acknowledge that our literature review did not include BoosTrack++ and OccluTrack. However, please understand that there are numerous studies related to MOT, and we have tried our best to keep our data fair and reliable .\n\nTo address these issues, we have included comparisons with these methods on the MOT 17 and MOT 20 datasets, and have introduced the HOTA evaluation metric. This will help in more comprehensively and accurately evaluating and demonstrating the performance of our model. It can be observed that our HOTA and MOTA scores are comparatively higher, while our IDF1 and ID Sw. scores are somewhat lower. Despite this, these outcomes clearly demonstrate the effectiveness of our method.\n\n| **Datasets** | **Methods**   | **HOTA** | **MOTA** | **IDF1** | **ID Sw.** |\n|--------------|---------------|----------|----------|----------|------------|\n| **MOT17**    | BoostTrack++  | 66.6     | 80.7     | 82.2     | 1,062      |\n|              | OccluTrack+   | 66.8     | 80.2     | **82.8**     | **951**        |\n|              | STAR          | **67.8** | **81.9** | 80.2     | 1,057      |\n| **MOT20**    | BoostTrack++  | 66.4     | 77.7     | 82.0     | 762        |\n|              | OccluTrack+   | 66.7     | 77.7     | **82.7**     | **429**        |\n|              | STAR          | **67.3** | **79.6** | 79.0     | 1,047      |\n\n\n---\n\n**Q3:** Provide the ablation study on the IoU-based Association.\n\n**Response to Q3:**\n\nWe realize the significance of this component within our tracking framework. \nWe have now conducted a detailed ablation study to evaluate the role of the IoU-based Association. \nDeepSORT*, JDE*, and CTracker* represent modified DeepSORT, JDE, and CTracker, which rely solely on IoU distance.\nResults from this study show that CITG perform best. We appreciate your suggestion and believe that this addition strengthens our paper significantly.\n\n| **Methods**         | **MOTA** | **IDF1** | **HOTA** | **ID Sw.** |\n|---------------------|----------|----------|----------|------------|\n| Deepsort*+STAR      | 58.8     | 65.2     | 54.3     | 1,315      |\n| JDE*+STAR           | 73.5     | 72.4     | 55.3     | 1,125      |\n| CTracker*+STAR      | 78.8     | 76.6     | 66.0     | 540        |\n| CITG+STAR           | **80.6** | **77.2** | **67.5** | **326**       |\n\nThank you once again for your valuable feedback, and we look forward to your further queries or suggestions."}, {"review_id": "zIVPoDG7Kb", "reviewer": "Reviewer_5M6K", "summary": "The paper proposes an MOT method under the tracking-by-detection framework.  It uses graph representation learning to explore spatial-temporal information within tracklets. And it uses tracklet clip-level graph matching to gain robustness to occlusions and ID-switches. It does experiments on three widely used benchmarks to verify its effectiveness.", "questions": "1)\tIt is suggested to resummarize its contribution to highlight its difference with existing tracklet graph matching-based methods works. \n2)\tUnclear technical and experiment details, to see the weakness.", "limitations": "yes.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength\n1）\tIts motivation is plausible and it describes clearly different modules of the method like graph construction and propagation.\n\n2）\tAblation studies on the tracklet clip graph construction and the STAR module are provided.\n\n3）\tIt selects a variety of challenging benchmarks to verify its effectiveness.\n\n4）\tSupplementary material provides more comparisons on MOT and UAVDT datasets.\n\nWeakness：\n--Overall, the idea is not new in MOT domain which solves viewpoint variations and occlusions by exploiting spatial-temporal information. \nFormulating the data association problem as a graph matching task is common in MOT. And the hierarchical association strategy from detections to tracklet clips is also commonly used. It is suggested to highlight the major differences between the existing tracklet graph matching-based methods and the proposed TCGM.\n\n--Some technique details:\nFor object that haven’t appeared in a frame in the TC, they use an empty placeholder. How the appearance and position features and the topological features of such dummy nodes are computed? How to process them in the following graph matching.\n\nThey use a weighted binary cross-entropy loss function for the differentiable graph matching. Though it refers to [31], some explanations about KKT conditions and the implicit function theorem can be provided in supplementary material.\n\n--Unclear experiment details:\n-In the ablation study on the effect of STAR, the experiment configurations are unclear. Adding STAR as a postprocessing step to an existing method seems sure to bring improvement.\n\n-How it determines to create a new trajectory or terminate an existing trajectory with the object disappearing for a long time. \n\n-- Since the method is verified in both public and private detection settings, it is suggested to list the two types separately in experiment results. Quality of detections greatly affects the tracking performance in the TBD framework.\n\n----The whole pipeline as in Fig. 2 is somewhat complex, which may hinder the proposed method from real application. Efficiency is an unavoidable problem in MOT, though this problem is briefly discussed in the limitation section.", "was_revised": true, "final_justification": "I carefully read the rebuttal and other reviewers’ comments. I will be happy to follow other reviewers’ opinions and have no counterview if this paper is accepted. However, I still consider its claim on contribution/novelty is incorrect. So my final rating is borderline.\n\n[24,a] both simultaneously introduce temporal information and topological relations.\n[24] includes an association graph to leverage the topological relationships both spatially and temporally.\n[a] considers temporal information and topological relations by defining a learnable interaction representation which captures the latent representations of all other tracklets whose lifespan overlaps with the temporal range of the target tracklet.\n\nIn fact, a MOT work will not consider topological relations alone but not consider temporal information.\n\nIt claims “Previous graph matching methods construct graphs for individual objects in each frame, resulting in  n*L graphs”.\nNote that many exiting graph matching methods construct graphs for tracklets, i.e. making associations between tracklets (the same as ‘tracklet clip’ in the paper), but NOT for individual objects in each frame. So their numbers of graphs for matching is similar to STAR. Both STAR and previous methods need initial tracklet generator, so computation complexity of this initialization part is also the same.", "rebuttal": "Dear Reviewer 5M6K,\n\nThank you for the detailed review and invaluable comments. We address your concerns point by point. Feel free to ask follow-up questions if something remains unclear.\n\n---\n\n**Q1:** It is suggested to resummarize its contribution to highlight its difference with existing tracklet graph matching-based methods works.\n\n\n**Respond to Q1:** \nThank you very much for the valuable suggestions. We have summarized the contribution of the STAR method again and clearly highlighted its differences from the existing tracklet graph matching-based methods. Firstly, unlike traditional graph-based MOT methods that consider each target independently, STAR considers not only the features of individual targets but also the interactions between targets (i.e., the topological relationships between targets). By establishing these relationships, STAR not only enhances the distinctiveness of individual target characteristics but also effectively addresses dynamic occlusion issues using the short-term stability in relationships between targets. This approach significantly improves tracking continuity and accuracy in complex environments.\n\nRegarding the innovation in data association, STAR employs a graph matching approach where each target's tracklet segment is treated as a node in the graph. This method, compared to traditional strategies that consider each individual target as a node, greatly reduces the number of graph matching operations, thus significantly improving processing efficiency. In specific experimental tests, this technique not only drastically reduces the computational load but also enhances the smoothness and accuracy of tracking.\n\n---\n\n**Q2:** Some technique details: For object that haven’t appeared in a frame in the TC, they use an empty placeholder. How the appearance and position features and the topological features of such dummy nodes are computed? How to process them in the following graph matching.\n\n**Respond to Q2:** \nFor object that haven’t appeared in a frame in the TC, we use an empty placeholder. Then the initial features are initialized with the average of its adjacent non-empty frames.\n\nAfter initialization, each tracklet segment is treated as a node, and the features of these nodes are iteratively updated through TCGC. Ultimately, $m $ feature graphs are obtained. \nBy performing graph matching on these feature graphs, the matching between tracklet segments is achieved.\n\nThe ablation study analyzing the impact of STAR was conducted on the MOT16 dataset. We demonstrated that adding STAR to existing methods can further enhance their performance. Compared to Deepsort, JDE, and CTracker, CTracker*/Deepsort*/JDE* indicate that trajectories are generated using only the Intersection over Union (IoU) distance. The introduction of the STAR module consistently improves the performance of existing methods. The experiments indicate that STAR is a model-independent, universal method that can be seamlessly integrated into existing technologies, providing continuous performance optimization."}]

Author rebuttal:

Meta-review: The paper received two accepts, one borderline accept, and one borderline reject. Reviewers #pCN3, #MVNd, and #pdkw supported the STAR framework’s reframing of the object association problem through tracklet clip graph matching, highlighting its technical novelty and empirical strengths. Reviewer #5M6K, however, remained skeptical, arguing that the claimed novelty was overstated given prior work on temporal and topological relations, and also raised concerns about technical clarity and efficiency.

In the rebuttal, the authors clarified the role of the Confident Initial Tracklet Generator, provided expanded ablation studies (including IoU-based associations), incorporated missing benchmarks and HOTA results, and discussed efficiency improvements with a Gated Search Tree. Reviewers #pCN3 and #MVNd were satisfied with these responses and maintained strong support. Reviewer #pdkw acknowledged the improvements but remained concerned that performance gains were driven more by detection quality than genuine advances in occlusion handling. Reviewer #5M6K accepted that technical details were clarified but continued to question the novelty claims, leaving their final recommendation at borderline reject.

After carefully reviewing the submission, rebuttal, and reviewer feedback, the AC notes that the novelty of the proposed method relative to prior work (e.g., SHUSHI [bit.ly/sushi-mot]) remains somewhat vague, and that STAR underperforms compared to certain offline tracking methods. Overall, the paper sits on the borderline. Nevertheless, given the majority of reviewers leaned positive, the AC does not intend to overturn this consensus and recommends acceptance. The authors are strongly encouraged to further clarify the originality of their contributions—particularly in comparison to SHUSHI—and to address the concerns raised by reviewers #pdkw and #5M6K in future revisions.
Decision: Accept (poster)