TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-06 23:18:44

Abstract: Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms. However, existing datasets for multi-object tracking in traffic videos often feature limited instances or focus on single classes, which cannot well simulate the challenges encountered in complex traffic scenarios. To address this gap, we introduce TrafficMOT, an extensive dataset designed to encompass diverse traffic situations with complex scenarios. To validate the complexity and challenges presented by TrafficMOT, we conducted comprehensive empirical studies using three different settings: fully-supervised, semi-supervised, and a recent powerful zero-shot foundation model Tracking Anything Model (TAM). The experimental results highlight the inherent complexity of this dataset, emphasising its value in driving advancements in the field of traffic monitoring and multi-object tracking.

Main paper: 
 trafficmot: unlocking multi-object tracking in complex traffic: 
 In this section, we delve into a comprehensive exploration of our dataset, providing detailed insights into its characteristics and statistical properties. We first introduce the data collection, annotation, and partitioning process. Then, we conduct statistical analyses to reveal the complexity of dataset.Video Collection. We collected videos from eight cities spanning different regions in India, including northern, southern, eastern, western, and central parts of the country. The video acquisition process involved the use of fixed CCTV cameras installed on roads and highways. These cameras provided a comprehensive view of the traffic scenarios in different urban and suburban settings.Our dataset consists of a total of 2,102 videos, where each video comprises 30 frames, allowing for a significant amount of temporal information to be extracted. To capture the heterogeneity of realworld surveillance systems, the videos were recorded using different cameras, resulting in varying frame resolutions, including 352 × 288, 1056 × 864, 1280 × 720, and 1920 × 1080. This diversity in frame resolutions reflects the practical challenges encountered in realworld traffic surveillance scenarios.Instance Annotation. The annotation process for our proposed TrafficMOT dataset involved providing instance bounding box (bbox) labels and categorising each instance into one of the ten classes illustrated in Figure 2 for each annotated frame. Out of the total 2,102 videos in the dataset, 78 videos were fully annotated, meaning that all 30 frames in each of these videos were annotated. In these fully annotated videos, TrafficMOT provides tracking ids for each instance, enabling the tracking of objects across frames within the video. For the remaining 2,024 videos, annotations were only provided for the first frame. This resulted in a total of 8,689 unannotated frames across the dataset. The presence of this large number of unlabelled frames presents an opportunity to explore and advance semi-supervised learning techniques that leverage a limited number of annotations.To ensure the quality of the annotations, a professional company was engaged to carry out the annotation process. Following the completion of the annotations, a dedicated quality control groupTractor E-rickshaw LCV Pedestrian Bike Auto LMV BusMotor Bike meticulously reviewed all the annotations. Any annotations identified as low quality were sent back to the company for refinement. This iterative process of annotation refinement was conducted multiple times to ensure a high level of annotation quality in the TrafficMOT dataset. Official Partition. To facilitate the multiple object tracking task, the TrafficMOT dataset was divided into training and test sets at the video level. Out of the 78 fully annotated videos, 51 videos were selected as the testing set. These videos contain instance-level annotations (bounding box + category) for every frame and provide instance tracking IDs that persist throughout the video.During the fully-supervised training process, we employed the remaining 27 fully annotated videos in conjunction with the 2,024 videos that only had annotations on the first frame. In contrast, the semi-supervised setting incorporated an additional 8,689 unlabelled frames as part of the training set. This inclusion of unlabelled data presents a chance to explore semi-supervised learning techniques that leverage both labelled and unlabelled data for improved performance. Partitioning the dataset into separate train and test sets ensures a rigorous evaluation of multi-object tracking performance and enables fair comparisons for different methods.Diverse Classes. Figure 2 presents annotation examples for each class, illustrating that certain classes exhibit a notable resemblance. For instance, distinguishing between the e-rickshaw and auto, as well as between the bike and motorbike, requires careful examination. Accurately identifying these classes is vital to ensure road safety, particularly considering the potential risks associated with the interaction between motor vehicles and non-motor vehicles. This underscores the importance of exploring complex traffic scenarios that involve interclass correlations.In Figure 4 , we present the correlation matrix depicting the presence of different classes within the same video. We divide the ten classes into two groups: motor and non-motor vehicles. The motor vehicle classes exhibit a strong correlation with each other indicating that different types of motor vehicles commonly cooccur in our dense and complex TrafficMOT dataset. Moreover, the correlation between motor vehicles and non-motor vehicles is shown in the pink region in Figure 4 . It is evident that the pedestrian and e-rickshaws classes (0.37 and 0.47) frequently appear alongside heavy vehicles such as tractors raising safety concerns for citizens. Hence, our TrafficMOT dataset is beneficial for analysing dense traffic conditions and vehicle behaviour patterns to mitigate the risks of vehicle accidents.Crowded Scenes in Multi-Object Tracking. Our dataset stands out from existing datasets in terms of traffic density, offering a unique opportunity to explore and tackle the challenges of multiobject tracking in dense traffic flow scenarios. While existing datasets are often limited in terms of the number of objects (as indicated in Table 1 , and detailed in Figure 3 ), our dataset presents a significant advancement by providing a rich and challenging environment with densely populated traffic scenes.A key characteristic of our dataset is the high average number of objects per frame. On average, our dataset contains 22.8 objects per frame, surpassing the ranges found in other datasets which average from 5.4 to 8.2 objects per frame, as shown in Table 1 . This notable difference in object density creates complex tracking scenarios, requiring advanced algorithms and techniques to accurately track and analyse multiple objects amidst congested traffic conditions. Why is Traffic Density Relevant? The heightened object density in our dataset imposes greater demands on tracking algorithms, requiring them to exhibit enhanced robustness, accuracy, and efficiency in handling a larger number of objects simultaneously. Furthermore, the dense scene characteristic exposes tracking algorithms to challenges such as occlusions, partial object visibility, and overlapping trajectories, all of which are prevalent in crowded traffic scenarios. This realistic representation of traffic conditions provides a more meaningful and representative benchmark for evaluating the performance of multi-object tracking algorithms in practical traffic analysis applications.Weather Conditions in TrafficMOT. Our dataset comprises videos captured under diverse weather conditions, providing a realistic representation of real-world scenarios. It encompasses sunny (daytime) weather (1,761 videos), foggy conditions (218 videos), and low-light conditions during nighttime (123 videos). This deliberate inclusion of various weather conditions enhances the training difficulty and exposes the model to a wider range of scenarios that it may encounter in the real world.The presence of videos captured under sunny weather conditions reflects typical daytime scenarios, enabling the model to learn patterns and behaviours under optimal lighting conditions. This serves as a baseline for evaluating the model's performance in ideal weather conditions. Incorporating videos captured in foggy conditions introduces challenges associated with reduced visibility, as shown in Figure 5  
benchmarking with trafficmot dataset on multiple object tracking: 
 We conduct a comprehensive benchmark on multi-object tracking (MOT) by evaluating three types of methods: fully-supervised MOT methods (Section 3.1), semi-supervised MOT methods (Section 3.2), and a recent powerful zero-shot foundation model, the Tracking Anything Model (Section 3.3). This extensive evaluation allowed us to assess the performance of various MOT approaches on our TrafficMOT dataset and gain valuable insights into their respective strengths. It is worth noting that, to the best of our knowledge, our work is the first to apply large-scale foundation models to multi-object tracking tasks in complex traffic scenarios.We first train fully-supervised MOT models using existing baseline MOT methods. Most baselines integrate an object detector followed by a tracker, so the training process is formulated asEQUATIONwhere ℒ 𝑑𝑒𝑡 represents the object detection and classification loss, typically employed by methods like Faster R-CNN [23] . ℒ𝑡𝑟𝑎 denotes object association or tracking loss, such as the loss used for learning instance similarity [20] . In our study, we specifically focus on four fully-supervised MOT methods: DeepSORT [29] , Byte-Track [34] , QDTrack [20] , and OC-SORT [2] .DeepSORT includes appearance information to SORT [1] to better track occluded objects. SORT, which uses the overlap of bounding boxes as a metric for object association, cannot effectively track through occlusions due to the simple association metric. DeepSORT thus integrates the appearance and motion information into the association metric to further improve SORT.ByteTrack makes full use of both the high-and low-score detection boxes for object association while most other MOT methods only utilise the high-score ones. It first associates the high-score detection boxes to certain tracklets based on their IoU similarity. After that, it matches the low-score detection boxes to the remaining unmatched tracklets.QDTrack (Quasi-Dense Tracking) adopts a two-step approach to enhance feature embedding and object association. Firstly, it employs quasi-dense contrastive learning, which involves densely matching extensive regions of interest in one image with corresponding regions in another. This process aims to learn a more robust feature embedding. Secondly, QDTrack uses bi-directional softmax to associate objects, ensuring bi-directional consistency. This means that the feature embeddings of two associated objects should be the nearest neighbors of each other.OC-SORT (Observation-Centric SORT) addresses noise accumulation in SORT [1] , which employs the Kalman filter (KF) and an object association matrix. OC-SORT proposes two strategies: Observation-centric Re-Update (ORU) and Observation-Centric Momentum (OCM). ORU rechecks untracked tracks associated with object tracklets and updates KF parameters accordingly. OCM introduces a cost matrix for object association, enforcing consistent motion direction among objects.In semi-supervised training setting, we incorporate the common pseudo-labelling mechanism for object detection to implement multi-object tracking. We apply three semi-supervised strategies across all four MOT baseline methods, namely STAC [25] , Soft-Teacher [31] , and MotionPrior [12] .STAC operates in two stages. In the first stage, an MOT model is trained using labelled training data. In the second stage, the trained MOT model is utilized to generate pseudo-labels for unlabelled data. Subsequently, the MOT model is re-trained using both the labelled and pseudo-labelled data, improving its performance through the inclusion of additional unlabelled samples.SoftTeacher shares a similar setting with STAC but introduces a single-stage strategy to perform the two-stage pseudo-labelling mechanism in an end-to-end manner.MotionPrior extends STAC by incorporating simple motion prior present in traffic datasets. Therefore, it can filter out noisy pseudo labels, thereby enhancing the quality of the training dataset.Tracking Branches in Semi-Supervised Training. The three aforementioned pseudo-labelling strategies are primarily designed for object detection, which may result in inaccurate pseudo-labels for tracking IDs. Consequently, in the context of semi-supervised MOT, it is necessary to ensure the consistency of pseudo-labels across the time series before utilizing them for training purposes.To this end, given a predicted object at a certain position specified by bounding box b𝑖 , we will find the nearest object to b𝑖 in the next frame, which is denoted as b𝑖+1 . Then, we check the consistency 𝐶 of these two objects viaEQUATIONwhere IoU(⋅, ⋅) is the function computing the intersection over the union score of two inputs, and 𝜏𝑐 is a threshold. If the predicted bounding boxes of these two objects are close to each other and their object identities are the same, then 𝐶 = 1. In this case, we regard them as the same tracking object and keep their predictions as pseudo-labels. In this way, we can obtain the pseudo-labels for all the unlabelled videos, for both detection and tracking results. Subsequently, we incorporate these pseudo-labelled videos alongside the labelled ones to train another MOT model, employing the same loss functions as defined in Equation (1).Additionally, we conduct empirical studies using the Tracking Anything Model (TAM) [32] , a recent and powerful zero-shot foundation model [10] . TAM demonstrates potential applicability in the context of semi-supervised MOT, further enhancing the versatility of our research.Track Anything Model, also named Segment and Track Anything (SAM-Track), is a powerful zero-shot foundation model for general MOT tasks such as unsupervised MOT, semi-supervised MOT, and interactive MOT. It adopts the latest Segment Anything Model (SAM) [10] to generate a segmentation mask for the first frame of videos with the help of user interactive clicks. Then it exploits XMem [3] , a semi-supervised MOT method requiring a precise mask to initialise, to output a predicted mask. If the mask is of poor quality, SAM is further used to refine the mask. Otherwise, the mask is accepted as the final predicted tracking mask.We consider four different ways to apply TAM for zero-shot multi-object tracking, as shown in Figure 7 . (a) We directly adopt TAM to track everything on TrafficMOT using default settings. (b) We make full use of the first-frame annotations in our dataset to guide the learning process of TAM to track traffic-related objects. (c) We input the class names in our dataset as the text prompts to guide the TAM to learn to track the objects of these classes. (d) We use both text prompts and first-frame annotations to better adapt TAM to our dataset. Through these empirical studies, we illustrate that even a large-scale MOT model such as TAM struggles to effectively handle the MOT task as defined in our dataset. These comprehensive studies serve as a testament to the challenges posed by complex traffic scenarios within our dataset. 
implementation details, preprocessing, & evaluation metrics: 
 Implementation Details. We implemented the MOT architecture based on MMTracking [5] framework using the PyTorch [21] library. For fair comparison with all the fully-supervised MOT methods [2, 20, 29, 34] , we implemented the object detection module with the same architecture: Faster R-CNN (Region-based Convolutional Neural Network) [23] incorporating with region proposal network (RPN) [11] on ResNet-50 [8] backbone with ImageNet [7] pre-trained weights. The models were trained for 15 epochs with the AdamW optimiser [13] by step decay initialling with a learning rate of 2 × 10 -4 . For the training of semi-supervised frameworks, we initialised their weights with the corresponding trained fullysupervised models. Further, we tuned the initial learning rate on AdamW optimiser to 5 × 10 -5 and trained an additional five epochs.The empirical studies on fully-supervised and semi-supervised methods are all trained on an NVIDIA A100 GPU with 80GB RAM for approximately six hours and twlve hours respectively. For the TAM experiments, we set the new object updating parameters as default in all four track anything settings following [32] .Preprocessing. We followed the data augmentation strategy in MMTracking to enhance the variety of the dataset. Specifically, we first resized the images to 1, 088 × 1, 088 while preserving the aspect ratio, then performed photometric distortion, random flipping, normalisation, and padding.Detection Evaluation. We measure the detection performance with the mAP (mean Average Precision) and mAP 50 metrics. mAP calculates the precision-recall curve by varying the detection threshold and averages the AP values for all classes, while mAP 50 takes the average precision at an IoU threshold of 0.5 for better evaluating moderate overlap objects.Tracking Evaluation. We evaluate the tracking performance with Multi-Object Tracking Accuracy (MOTA) and Identity F1 score (IDF1). MOTA can assess the overall tracking accuracy by considering false positive, false negative, and tracking ID switches. IDF1 measures the accuracy of tracking identity by the F1 score based on the overlap between predicted and ground truth tracks.Fully-Supervised Results. We benchmark four fully-supervised MOT methods, namely DeepSORT [29] , ByteTrack [34] , QDTrack [20] , and OC-SORT [2] ; as shown in Table 2 and Figure 6 .DeepSORT demonstrates relatively poor performance, mainly due to its challenges in handling occlusions, which are common complexities in traffic scenarios. On the other hand, ByteTrack [34] achieves the best performance in terms of mAP, mAP 50 , and IDF1, with scores of 0.463, 0.651, and 0.637, respectively. In Figure 6 , Byte-Track accurately detects bounding boxes but faces difficulties in establishing consistent connections across frames for tracking. Despite its limitations in detection, QDTrack outperforms ByteTrack in terms of MOTA by 0.088, indicating its superior ability to process tracking information. However, OC-SORT, being a newer method, exhibits below-average performance in terms of MOTA, suggesting it may not be well-suited for traffic scenarios. Semi-Supervised Results. In addition to benchmarking supervised methods, we also evaluate three semi-supervised pseudolabelling strategies across all four baseline MOT models, the results of which are presented in Table 3 . Upon initial analysis, it is observed that MotionPrior generally outperforms the other two strategies, and STAC marginally surpasses SoftTeacher. When compared to the fully-supervised setting, all semi-supervised strategies yield lower scores in terms of mAP and mAP 50 , which indicates reduced detection accuracy. However, models like DeepSORT, ByteTrack, and OC-SORT exhibit improved IDF1 and MOTA scores, which indicates improved tracking performance. Yet, even though the semi-supervised setting can help in improving tracking accuracy, the overall performance remains relatively low due to the dataset's complexity. Consequently, the design and development of further algorithms tailored to manage such challenging datasets is imperative.TAM Results. Although TAM has demonstrated superior performance in interactive object tracking and segmentation in some videos [32] , it yields unsatisfactory results on our TrafficMOT dataset, as depicted in Figure 7 . We analyse the experimental results for five different TAM settings, as explained in Sec. 3.3. In the (a) track Everything mode, TAM outputs trajectories for all objects, including background and irrelevant objects, leading to a loss of focus on traffic-related objects. The second row in Figure 7 displays the tracking results when using the first frame annotation to guide the tracking of subsequent objects. It is evident that this model performs well only on the initial frames, and then transitions towards the (a) Everything mode for the remaining frames. From the third row, we observe that using class names as prompts allows TAM to generate stable tracking outputs for vehicles and pedestrians, but with numerous missing objects in crowded areas. When integrating the (d) first frame annotation and class name to guide TAM, the output outperforms all other settings. However, it may still generate unusual objects, as depicted by the large green bounding box in the last column. Moreover, new objects, such as the white car in the bottom-left corner visible in the third and fourth frames, are not tracked until the fifth frame. A critical observation across all four settings is that while objects can be tracked, they are not accurately recognised. All instances are generalised as 'objects' without specific class designations. Despite TAM's promising pixel-level annotation, it falls short in providing precise class information. 
conclusion: 
 This study addresses the vital area of multi-object tracking in traffic videos, recognising its potential to enhance traffic monitoring and promote road safety through advanced machine learning algorithms. We acknowledge the limitations of current datasets, which often oversimplify the task and lack adequate instances or diverse class representation. To tackle these challenges, we present TrafficMOT, a comprehensive and diverse dataset specifically designed to cover a wide range of complex traffic scenarios. Our research includes three empirical studies: fully-supervised, semi-supervised, and the recent zero-shot foundation model (TAM). These studies underscore the inherent complexity and difficulties of our TrafficMOT dataset. Our experimental findings highlight the inherent difficulty of our dataset while also showcasing its potential to drive significant advancements in the fields of traffic monitoring and multi-object tracking. With its high level of complexity and diversity, we envision that TrafficMOT will serve as a challenging yet realistic benchmark for the development and refinement of future multi-object tracking algorithms. 
: 
 crucial for accurately reflecting real-world traffic scenarios, such as occlusions, varying lighting conditions, diverse traffic patterns, and importantly dense scenes. Consequently, the need for a new dataset arises, one that investigates the challenges encountered in complex traffic environments.Current Datasets in Traffic Analysis and Their Limitations. Two widely used datasets in the field of traffic analysis are the VERI-Wild dataset [14] by Lou et al. and the MOTS dataset [28] by Voigtlaender et al. The VERI-Wild dataset focuses on vehicle re-identification in urban scenes and consists of data from 174 cameras. While it provides a comprehensive view of vehicle tracking scenarios, it is not specifically designed for multi-object tracking in intricate traffic scenarios. Similarly, the MOTS dataset extends multi-object tracking by incorporating object segmentation, but it also lacks a specific focus on complex traffic scenarios. Moreover, both datasets are not specifically designed for multi-object tracking in the context of intricate traffic scenarios.Figure 1 and Table 1 provide the comparisons between our TrafficMOT and other datasets in the field of traffic multi-object tracking. Note that the Urban Tracker [9] is introduced in 2014, focusing on multiple object tracking in urban mixed traffic. However, this dataset consists of only five collected video sequences, limiting its diversity and scalability. Later, Shah et al. [24] present a CADP dataset for traffic accident analysis using CCTV traffic cameras. While the dataset contain 1,416 video segments with full spatio-temporal annotations, it is specifically tailored to accident analysis and does not cover the broader challenges of multi-object tracking in traffic. Recently, CityFlow [27] is introduced for vehicle tracking and identification, providing a valuable dataset for research in the field. However, the number of classes is limited and it is unable to handle dense traffic scenarios. Most recently, improved versions of CityFlow are released in [18] , where a series of AI city challenges are held for multi-camera people tracking, tracked vehicle retrieval by natural language, and traffic safety [19] . While the above datasets are specifically tailored for MOT in traffic scenes, significant limitations still persist, including limited class numbers and traffic density.Contributions. In this paper, we present TrafficMOT, a novel benchmark dataset that introduces new challenges in the field of multi-object tracking in traffic analysis. Acquired from fixed CCTV cameras across a wide range of cities, TrafficMOT stands out among existing datasets due to its unique and challenging characteristics. Notably, our dataset offers an extensive number of object classes, surpassing any other existing dataset. Additionally, TrafficMOT provides dense scenes, mainly capturing the intricate dynamics and congestion found in traffic scenarios. To highlight the complexity and challenges inherent in TrafficMOT, we also conduct an empirical study with three representative settings: fully supervised, semi-supervised, and a recent powerful zero-shot foundation model [10] named as Tracking Anything Model (TAM) [32] .Our experimental results underscore the inherent complexity of this dataset, demonstrating its potential to drive advancements in the field of traffic monitoring and multi-object tracking. Our contributions are summarised as follows.• We introduce TrafficMOT, a novel benchmark dataset specifically designed for multi-object tracking in complex traffic scenarios. This dataset offers a larger number of object classes and encompasses intricate traffic situations, including densely populated scenarios. • We conduct empirical studies with three representative settings: fully-supervised, semi-supervised, and a recent powerful zero-shot foundation model named as Tracking Anything Model (TAM). Experimental results highlight the intrinsic challenges posed by this dataset, thus emphasising its potential to spur advancements in traffic monitoring and road safety. 
 
 Appendix:  


Decision: The reviewers have come up with the following strengths and limitations of the paper

STRENGTH
- Comprehensive and Detailed Annotations
- Diverse and Challenging Dataset
- Good Evaluation Settings
- Thorough Description of Data Collection and Annotation
- Significant Contribution to MOT Research

LIMITATIONS
- Small dataset size
- Lack of Novelty: The dataset does not introduce any new problems for multi-object tracking. Three existing approaches are employed, with no novel approach being proposed. There is no contribution to algorithm design beyond the dataset itself.
- Comparison with Existing Datasets: The paper does not highlight significant improvements or unique features that differentiate TrafficMOT from existing datasets.
- Limited data collection information
- Writing and Structure issues
- Scalability Challenges: The high object density in the dataset poses scalability challenges for MOT algorithms
- Outdated Evaluation Methods

None of reviewers joined the rebuttal and changed their rating. However, the rebuttal did answer some questions raised by the reviewers well.

Reviews: review: paper_summary: paper_summary: This paper presents a new dataset for multi-object tracking in traffic videos. The objects are annotated. Three different approaches are employed to benchmark the dataset.
strength_weakness: strength_weakness: Compared with existing datasets, more objects and classes are included and annotated in the proposed one.
Detailed annotations are provided.
Different settings are explored in the benchmark.
limitations: limitations: Basically this is still a relatively small dataset of only 105 minutes' videos. Compared with existing datasets, there is no significant difference. The dataset also doesn't pose any new problems for multi-object tracking.
Three existing approaches are employed with no novel approach being proposed.
paper_summary: paper_summary: This paper proposes TrafficMOT, a dataset for multi-target tracking in traffic scenes. Compared with previous traffic scene multi-target tracking data sets, TrafficMOT covers a wider range of categories, more complex scenes, and can better simulate real scenes. Afterwards, the author conducted experimental verification in the form of fully supervised, semi-supervised and zero-shot.
strength_weakness: strength_weakness: (1)TrafficMOT includes a large number of object categories and contains complex traffic situations. Compared with previous related data sets, TrafficMOT is more challenging and can better model the real traffic environment.
(2) TrafficMOT uses three representative settings (fully supervised, semi-supervised and zero-shhot foundation model) for experimental verification. The experiment proved the effectiveness of the mothod.
limitations: limitations: In the multi-target tracking data set of traffic scenes, BDD100K also contains many categories. Compared with BDD100K, what are the advantages of TrafficMOT?
paper_summary: paper_summary: This paper introduce TrafficMOT, a novel benchmark dataset specifically designed for multi-object tracking in complex traffic scenarios. This dataset offers a larger number of object classes and encompasses intricate traffic situations, including densely populated scenarios.
strength_weakness: strength_weakness: 1. A multi-object tracking dataset in traffic scenes is proposed, and it is evaluated among multiple methods.
2. The evaluation of this dataset is comprehensive.
limitations: limitations: 1. There is no contribution to algorithm design apart from the dataset. 
2. There is limited introduction to the data collection process, lacking specific information on the number of samples for each category. 
3. In terms of writing, the introduction should focus more on background and motivation, preferably without placing Table 1 in the introduction section.
paper_summary: paper_summary: TrafficMOT introduces a novel and challenging dataset that captures the intricacies of complex traffic scenarios, aiming to promote research and development in the field of multi-object tracking for traffic analysis and road safety applications. The dataset presents challenges such as object occlusions, diverse traffic patterns, and intricate interactions between motor and non-motor vehicles, reflecting real-world complexities in traffic monitoring. Comprehensive empirical studies are conducted using three settings: fully-supervised, semi-supervised, and a recent powerful zero-shot foundation model called Tracking Anything Model (TAM). The experimental results highlight the inherent challenges posed by the TrafficMOT dataset.
strength_weakness: strength_weakness: 1) The introduction of the TrafficMOT dataset is a significant contribution. It addresses the limitations of existing datasets by providing a diverse set of videos from various regions, under different weather conditions, and with varying traffic densities. This makes it a valuable resource for advancing multi-object tracking (MOT) research in complex traffic scenarios. Besides, it includes real-world challenges such as occlusions, varying lighting conditions, and high object density. This realistic representation enhances the relevance and applicability of the dataset for practical traffic monitoring systems.

2) The paper provides a thorough description of the data collection, annotation process, and dataset partitioning. This level of detail ensures reproducibility and transparency, which are critical for benchmarking and further research.

3) The comprehensive empirical studies using fully-supervised, semi-supervised, and zero-shot foundation models (TAM) provide valuable insights into the performance of different MOT approaches on the TrafficMOT dataset. This helps in understanding the strengths and limitations of current methods.
limitations: limitations: 1) The paper primarily focuses on the dataset and empirical evaluations but does not introduce novel algorithms or significant modifications to existing methods. This may limit its impact compared to papers that contribute new techniques or improvements.

2) The scale of the data is not significantly larger than before.

3) The high object density in the dataset poses scalability challenges for MOT algorithms. The paper could explore more scalable approaches or provide recommendations for handling such dense scenes more effectively.

4) The fully-supervised and semi-supervised methods used for evaluation are simple and outdated.