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3d multi-object tracking with boosting data association and improved trajectory management mechanism

SIGNAL PROCESSING 2024年 218卷

作者： Jin, Jingyi Zhang, Jindong Zhang, Kunpeng Wang, Yiming Ma, Yuanzhi Pan, dongyu Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China

In the multi-object tracking (MOT) algorithm based on the tracking-by-detection paradigm, matching accuracy between detection and prediction, robustness to occlusion and consistency between trajectory and ground truth are three emphasized issues. In this study, we propose a tracking-by-detection-based online 3d MOT framework, which produces 3d bounding boxes with identity, position, and shape in real time from point clouds provided in each sequential frame. Specifically, first, to increase the correct matching rate, we propose a 3d efficient affinity (3d-EAFF) that combines position correlation and geometric features to optimize data association between predictions and actual detections. Second, to reduce the identity switches caused by occlusion, a trajectory deletion strategy is proposed by using multiple distance thresholds to judge occlusion as well as setting a decay coefficient to eliminate prediction error accumulation. Third, to enhance the smoothness of the trajectory, a trajectory update strategy based on the position relationship between detection and prediction is proposed. By reallocating weights between measurements and prior estimates for Kalman filter updates, we aim to achieve a more optimal dynamic balance. The experimental results on two representative 3d MOT benchmarks NuScenes and KITTI show that our method effectively improves the tracking accuracy, and its robustness to occlusion also reaches competitive performance.

关键词： 3d multi-object tracking Autonomous driving tracking-by-detection Affinity metric Kalman filter

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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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deep Learning-Based 3d multi-object tracking Using multimodal Fusion in Smart Cities

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HUMAN-CENTRIC COMPUTING ANd INFORMATION SCIENCES 2024年 14卷

作者： Li, Hui Liu, Xiang Jia, Hong Ahanger, Tariq Ahamed Xu, Lingwei Alzamil, Zamil Li, Xingwang Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao Peoples R China Minist Educ Engn Res Ctr Integrat & Applicat Digital Learning Beijing Peoples R China Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Comp Sci Al Majmaah Saudi Arabia Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo Peoples R China

The intelligent processing of visual perception information is one of the core technologies of smart cities. deep learning-based 3d multi-object tracking is important in improving the intelligence and safety of robots in smart cities. However, 3d multi-object tracking still faces many challenges due to the complexity of the environment and uncertainty of the object. In this paper, we make the most of the multimodal information of image and point cloud and propose a multimodal adaptive feature gating fusion module to improve the feature fusion effect. In the object association stage, we designed an orientation-position-aware affinity matrix (EO-IoU) by using Euclidean distance, orientation similarity, and intersection over union, which is more suitable for the association to solve the problem of association failure when there is little or no overlap between the detection box and the prediction box. At the same time, we adopt a more robust two-stage data association method to solve the trajectory fragmentation and identity switching caused by discarding low-scoring detection boxes. The results of extensive experiments on the KITTI and NuScenes benchmark datasets demonstrate that our method outperforms existing state-of-the-art methods with better robustness and accuracy.

关键词： Smart Cities Visual Perception 3d multi-object tracking multimodal Feature Fusion Position Affinity Matrix data Association

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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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Monocular 3d object detection and 3d multi-object tracking for Autonomous Vehicles

Monocular 3D Object Detection and 3D Multi-Object Tracking f...

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作者： Reading, Cody Ariel University of Toronto (Canada)

学位级别：M.A.S., Master of Applied Science/Accounting Science/Applied Statistics/Aeronautical Science/Advanced Studies/ Master of Arts in Science

In this thesis, we first tackle the monocular 3d object detection task. The main challenge in monocular 3d detection lies in predicting depth, which must be inferred due to the lack of range measurement. We propose CaddN, which predicts a categorical depth distribution for each pixel to project feature information in 3d space. We then use the bird's-eye-view projection and single-stage detector to produce the final output detections. Next, we tackle the 3d multi-object tracking task, where associating tracks to detections remains challenging. Our proposed solution, InterTrack, introduces the Interaction Transformer to generate discriminative object representations for data association. We extract track and detection features, and aggregate global information via attention. We then use each track/detection feature pair to estimate affinities, and use a two-stage data association and track management approach to produce the final tracks. We perform validation on autonomous vehicle datasets and achieve state-of-the-art performance.

关键词： Autonomous vehicles 3d multi-object tracking 3d object detection

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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 2113-2113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 8650-8650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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