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mmMCL3dMOT: multi-Modal Momentum Contrastive Learning for 3d multi-object tracking

IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1895-1899页

作者： Hong, Ru Yang, Jiming Zhou, Weidian da, Feipeng Southeast Univ Sch Automation Minist Educ Nanjing 210096 Peoples R China Southeast Univ Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing Peoples R China

3d multi-object tracking methods utilize object motion, image, and point cloud information to compute similarities between different objects, facilitating cross-frame data association. In this letter, we propose a novel approach called mmMCL3dMOT to calculate object appearance similarity by employing multi-modal momentum contrastive self-supervised learning. We introduce three key techniques. First, a self-supervised training paradigm is adopted, incorporating image, point cloud, and existing 3d detection inputs to enable multi-modal feature extraction without manual annotation. Second, our feature learning approach combines intra-modal and cross-modal feature correspondences within image and point cloud modalities, resulting in more discriminative feature extraction with momentum contrast. Finally, by computing similarity using the multi-modal features and incorporating a robust motion metric, we enable joint cascade reasoning for object association, leading to high-performance 3d MOT. Extensive experiments have demonstrated the significant impact of our method. Moreover, our tracker achieves state-of-the-art (SOTA) performance on both the KITTI and nuScenes datasets.

关键词： Three-dimensional displays Point cloud compression Feature extraction Costs Contrastive learning Training Solid modeling 3d multi-object tracking multi-modal momentum contrast self-supervised learning

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Integrated detection and tracking Framework for 3d multi-object tracking in Vehicle-Infrastructure Cooperation

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INTERNATIONAL JOURNAL OF AdVANCEd COMPUTER SCIENCE ANd APPLICATIONS 2024年第11期15卷 1228-1237页

作者： Hu, Tao Wang, Ping Wang, Xinhong Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China

Vehicle-infrastructure cooperative perception has emerged as a promising approach to enhance 3d multi-object tracking by leveraging complementary data from vehicle and infrastructure sensors. However, existing methods face significant challenges, including difficulty in handling occlusions, suboptimal identity association, and inefficiencies in trajectory management, limiting their performance in real-world scenarios. In this paper, we propose a novel vehicle-infrastructure cooperative 3d multi-object tracking framework that addresses these challenges through three key innovations. First, an integrated detection- tracking framework jointly optimizes object detection and tracking, enhancing temporal consistency and reducing errors caused by separately handling the two tasks. Second, the XIOU identity association metric leverages 3d spatial and geometric relationships, ensuring robust object matching even under occlusions. Third, a four-stage cascade matching (FSCM) strategy adaptively manages trajectories by leveraging detection and prediction confidences, enabling accurate tracking in complex environments. Evaluated on the V2X-Seq dataset, our method achieves a MOTH of 57.23 and a MOTP of 74.64, significantly reducing identity switches while ensuring low bandwidth consumption and reliable tracking, highlighting its effectiveness and suitability for real- world deployment.

关键词： Vehicle-infrastructure cooperative perception 3d multi-object tracking XIOU metric four-stage cascade matching integrated detection-tracking framework

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SCMOT: Improving 3d multi-object tracking via Semantic Inference and Confidence Optimization 36

SCMOT: Improving 3D Multi-Object Tracking via Semantic Infer...

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36th Chinese Control and decision Conference (CCdC)

作者： Zhao, Lin Wang, Meiling Yue, Yufeng Beijing Inst Technol Sch Automat Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

3d multi-object tracking (MOT) is a fundamental technology in autonomous systems, playing a pivotal role across applications like autonomous driving and intelligent transportation systems. Previous 3d MOT methods mainly rely on LidAR point clouds for object detection and tracking, often facing challenges such as occlusions and sparse data. This paper introduces SCMOT, a novel multi-modal 3d MOT framework designed to address the limitations of existing LidAR-based 3d MOT methods. SCMOT enhances 3d object detection by filtering and refining results using semantic information, thereby reducing erroneous or redundant detections. To improve data association and enhance tracking precision, a multi-modal cost function that combines prediction confidence, semantic cues, and distance information is presented. Moreover, SCMOT can be served as a plug-and-play solution, integrating with diverse point cloud-based 3d object detectors. Extensive experiments on the KITTI tracking dataset validate the feasibility and effectiveness of SCMOT in real-world autonomous driving scenarios.

关键词： 3d multi-object tracking multi-modal autonomous driving

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PolarMOT: How Far Can Geometric Relations Take us in 3d multi-object tracking? 1

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17th European Conference on Computer Vision (ECCV)

作者： Kim, Aleksandr Braso, Guillem Osep, Aljosa Leal-Taixe, Laura Tech Univ Munich Munich Germany

ISBN: (数字)9783031200472

ISBN: (纸本)9783031200465;9783031200472

Most (3d) multi-object tracking methods rely on appearance-based cues for data association. By contrast, we investigate how far we can get by only encoding geometric relationships between objects in 3d space as cues for data-driven data association. We encode 3d detections as nodes in a graph, where spatial and temporal pairwise relations among objects are encoded via localized polar coordinates on graph edges. This representation makes our geometric relations invariant to global transformations and smooth trajectory changes, especially under non-holonomic motion. This allows our graph neural network to learn to effectively encode temporal and spatial interactions and fully leverage contextual and motion cues to obtain final scene interpretation by posing data association as edge classification. We establish a new state-of-the-art on nuScenes dataset and, more importantly, show that our method, PolarMOT, generalizes remarkably well across different locations (Boston, Singapore, Karlsruhe) and datasets (nuScenes and KITTI).

关键词： 3d multi-object tracking Graph neural networks Lidar scene understanding

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Relation3dMOT: Exploiting deep Affinity for 3d multi-object tracking from View Aggregation

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SENSORS 2021年第6期21卷 2113页

作者： Chen, Can Zanotti Fragonara, Luca Tsourdos, Antonios Cranfield Univ Sch Aerosp Transport & Mfg Bedford MK43 0AL England

Autonomous systems need to localize and track surrounding objects in 3d space for safe motion planning. As a result, 3d multi-object tracking (MOT) plays a vital role in autonomous navigation. Most MOT methods use a tracking-by-detection pipeline, which includes both the object detection and data association tasks. However, many approaches detect objects in 2d RGB sequences for tracking, which lacks reliability when localizing objects in 3d space. Furthermore, it is still challenging to learn discriminative features for temporally consistent detection in different frames, and the affinity matrix is typically learned from independent object features without considering the feature interaction between detected objects in the different frames. To settle these problems, we first employ a joint feature extractor to fuse the appearance feature and the motion feature captured from 2d RGB images and 3d point clouds, and then we propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames and learn a deep affinity matrix for further data association. We finally provide extensive evaluation to reveal that our proposed model achieves state-of-the-art performance on the KITTI tracking benchmark.

关键词： 3d multi-object tracking sensor fusion deep affinity relation learning neural network

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MSA-MOT: multi-Stage Association for 3d multimodality multi-object tracking

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SENSORS 2022年第22期22卷 8650页

作者： Zhu, Ziming Nie, Jiahao Wu, Han He, Zhiwei Gao, Mingyu Hangzhou Dianzi Univ Sch Elect Informat Hangzhou 310018 Peoples R China Zhejiang Prov Key Lab Equipment Elect Hangzhou 310018 Peoples R China

Three-dimensional multimodality multi-object tracking has attracted great attention due to the use of complementary information. However, such a framework generally adopts a one-stage association approach, which fails to perform precise matching between detections and tracklets, and, thus, cannot robustly track objects in complex scenes. To address this matching problem caused by one-stage association, we propose a novel multi-stage association method, which consists of a hierarchical matching module and a customized track management module. Specifically, the hierarchical matching module defines the reliability of the objects by associating multimodal detections, and matches detections with trajectories based on the reliability in turn, which increases the utilization of true detections, and, thus, guides accurate association. Then, based on the reliability of the trajectories provided by the matching module, the customized track management module sets maximum missing frames with differences for tracks, which decreases the number of identity switches of the same object and, thus, further improves the association accuracy. By using the proposed multi-stage association method, we develop a tracker called MSA-MOT for the 3d multi-object tracking task, alleviating the inherent matching problem in one-stage association. Extensive experiments are conducted on the challenging KITTI benchmark, and the results show that our tracker outperforms the previous state-of-the-art methods in terms of both accuracy and speed. Moreover, the ablation and exploration analysis results demonstrate the effectiveness of the proposed multi-stage association method.

关键词： 3d multi-object tracking multimodal multi-stage association track management

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AnchorPoint: Query design for Transformer-Based 3d object detection and tracking

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第10期24卷 10988-11000页

作者： Liu, Hao Ma, Yanni Wang, Hanyun Zhang, Chaobo Guo, Yulan Sun Yat sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518107 Peoples R China Informat Engn Univ Sch Surveying & Mapping Zhengzhou 450001 Peoples R China Pengcheng Lab Shenzhen 518000 Peoples R China

With the success of Transformers in natural language processing, object detection with Transformers (dETR) has attracted widespread attentions. In previous Transformer-based 2d detectors, the object queries are a set of learning embeddings. However, it is very hard to apply these detectors to the 3d domain due to the lack of explicit physical meanings and position priors of learned object queries. In this paper, we introduce the concept of anchors and propose a novel query design based on anchor points. In our query design, we use the foreground points as the anchor points and encode these anchor points as the object queries. Consequently, each object query has an explicit physical meaning and only focus on its nearby object. Additionally, we also propose an instance-aware sampling strategy to select a small set of representation foreground points from the scene point cloud. Extensive experiments on several large-scale 3d object detection datasets demonstrate that the proposed AnchorPoint detector achieves promising accuracy and efficiency. In particularly, AnchorPoint achieves an average precision (AP) of 83.21 at 61 frame-per-second (FPS) on the moderate level of the KITTI-dET Car subset. Moreover, we model each object as its corresponding anchor point, and extend the AnchorPoint model to 3d multi-object tracking by adding an extra tracking head. We show that our method achieves comparable performance to existing state-of-the-art methods on the KITTI-MOT dataset.

关键词： Three-dimensional displays Transformers Point cloud compression object detection detectors Feature extraction Proposals Query design transformer Index Terms 3d object detection 3d multi-object tracking

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CC-3dT: Panoramic 3d object tracking via Cross-Camera Fusion 6

CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion

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6th Conference on Robot Learning (CoRL)

作者： Fischer, Tobias Yang, Yung-Hsu Kumar, Suryansh Sun, Min Yu, Fisher Swiss Fed Inst Technol Zurich Switzerland Natl Tsing Hua Univ Hsinchu Taiwan

To track the 3d locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings. Yet, camera-based 3d object tracking methods prioritize optimizing the single-camera setup and resort to post-hoc fusion in a multi-camera setup. In this paper, we propose a method for panoramic 3d object tracking, called CC-3dT, that associates and models object trajectories both temporally and across views, and improves the overall tracking consistency. In particular, our method fuses 3d detections from multiple cameras before association, reducing identity switches significantly and improving motion modeling. Our experiments on large-scale driving datasets show that fusion before association leads to a large margin of improvement over post-hoc fusion. We set a new state-of-the-art with 12.6% improvement in average multi-object tracking accuracy (AMOTA) among all camera-based methods on the competitive NuScenes 3d tracking benchmark, outperforming previously published methods by 6.5% in AMOTA with the same 3d detector. Project page: ***/pub/cc-3dt.

关键词： Autonomous driving 3d multi-object tracking Cross-camera fusion

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CenterTube: tracking multiple 3d objects With 4d Tubelets in dynamic Point Clouds

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IEEE TRANSACTIONS ON multiMEdIA 2023年 25卷 8793-8804页

作者： Liu, Hao Ma, Yanni Hu, Qingyong Guo, Yulan Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518107 Peoples R China Univ Oxford Dept Comp Sci Oxford England Natl Univ Def Technol Coll Elect Sci & Technol Changsha Peoples R China

3d multi-object tracking (MOT) in dynamic point cloud sequences is a fundamental research problem for several downstream tasks such as motion planning and action recognition. Existing methods usually rely on the traditional tracking-by-detection (TBd) paradigm, which performs the tracking based on the results achieved by dedicated detectors. However, this two-stage framework usually cannot sufficiently exploit spatial-temporal information and end-to-end optimization, leading to sub-optimal tracking performance, especially when the object is partially or completely occluded. In this article, we propose a joint detection and tracking framework named CenterTube for dynamic point cloud sequences. The key to our approach is to formulate the problem of multiple object trajectory predictions as 4d tubelet detections. In particular, the proposed CenterTube is composed of three head branches, including a center branch, a regression branch, and a movement branch for the estimation of object center, object size, instance movement, and frame interval, respectively. Additionally, a Tube BEV-IoU (TB-IoU) is also presented to link the generated clip-level tubelets and form the final tracks. Extensive experiments conducted on the KITTI-MOT and nuScenes datasets demonstrate that our model achieves competitive performances even if no ready-made detection results is adopted.

关键词： 3d multi-object tracking point cloud sequence 4d tubelet

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PTMOT: A Probabilistic multiple object Tracker Enhanced by Tracklet Confidence for Autonomous driving

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Automotive Innovation 2022年第3期5卷 260-271页

作者： Kun Jiang Yining Shi Taohua Zhou Mengmeng Yang diange Yang State Key Lab of Automotive Safety and Energy Center for Intelligent Connected Vehicles and TransportationSchool of Vehicle and MobilityTsinghua UniversityLee Shau Kee Building No A639Beijing 100084China

Real driving scenarios,due to occlusions and disturbances,provide disordered and noisy measurements,which makes the task of multi-object tracking quite *** approach is to find deterministic data association;however,it has unstable performance in high clutter *** paper proposes a novel probabilistic tracklet-enhanced multiple object tracker(PTMOT),which integrates Poisson multi-Bernoulli mixture(PMBM)filter with confidence of *** proposed method is able to realize efficient and robust probabilistic association for 3d multi-object tracking(MOT)and improve the PMBM filter’s continuity by smoothing single target hypothesis with global *** consists of two key ***,the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered ***,the confidence of tracklets is smoothed through a smoothing-while-filtering *** MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities.

关键词： 3d multi-object tracking Random finite set Probabilistic association Tracklet confidence smoothing

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