Papers 3D Multi-Object Tracking
“3D Multi-Object Tracking” 태그가 달린 논문 113편 · 필터 해제
SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection
In this work, we present SpaRC, a novel Sparse fusion transformer for 3D perception that integrates multi-view image semantics with Radar and Camera point features. The fusion of radar and camera modalities has emerged a…
3D Multi-Object Tracking3D Object DetectionAutonomous DrivingDepth Estimation+3CrossTracker: Robust Multi-modal 3D Multi-Object Tracking via Cross Correction
The fusion of camera- and LiDAR-based detections offers a promising solution to mitigate tracking failures in 3D multi-object tracking (MOT). However, existing methods predominantly exploit camera detections to correct t…
3D Multi-Object TrackingMulti-Object TrackingObject Tracking3D Multi-Object Tracking with Semi-Supervised GRU-Kalman Filter
3D Multi-Object Tracking (MOT), a fundamental component of environmental perception, is essential for intelligent systems like autonomous driving and robotic sensing. Although Tracking-by-Detection frameworks have demons…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject+1SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking
While existing query-based 3D end-to-end visual trackers integrate detection and tracking via the tracking-by-attention paradigm, these two chicken-and-egg tasks encounter optimization difficulties when sharing the same …
3D Multi-Object TrackingContrastive LearningDecoderMulti-Object Tracking+33D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving
The MS-GLMB filter offers a robust framework for tracking multiple objects through the use of multi-sensor data. Building on this, the MV-GLMB and MV-GLMB-AB filters enhance the MS-GLMB capabilities by employing cameras …
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject+1Open3DTrack: Towards Open-Vocabulary 3D Multi-Object Tracking
3D multi-object tracking plays a critical role in autonomous driving by enabling the real-time monitoring and prediction of multiple objects' movements. Traditional 3D tracking systems are typically constrained by predef…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject+1MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which …
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingMultiple Object Tracking+1RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework
3D Multi-Object Tracking (MOT) obtains significant performance improvements with the rapid advancements in 3D object detection, particularly in cost-effective multi-camera setups. However, the prevalent end-to-end traini…
3D Multi-Object Tracking3D Object DetectionComputational EfficiencyMulti-Object Tracking+4RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network
Perceiving the surrounding environment is a fundamental task in autonomous driving. To obtain highly accurate perception results, modern autonomous driving systems typically employ multi-modal sensors to collect comprehe…
3D Multi-Object Tracking3D Object DetectionAutonomous DrivingMulti-Object Tracking+5YOLOO: You Only Learn from Others Once
Multi-modal 3D multi-object tracking (MOT) typically necessitates extensive computational costs of deep neural networks (DNNs) to extract multi-modal representations. In this paper, we propose an intriguing question: May…
3D Multi-Object TrackingMulti-Object TrackingObject TrackingInScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios
Perception systems of autonomous vehicles are susceptible to occlusion, especially when examined from a vehicle-centric perspective. Such occlusion can lead to overlooked object detections, e.g., larger vehicles such as …
3D Multi-Object Tracking3D Object DetectionWeakly Supervised 3D DetectionBoosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check
In the domain of autonomous driving, the integration of multi-modal perception techniques based on data from diverse sensors has demonstrated substantial progress. Effectively surpassing the capabilities of state-of-the-…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject Tracking+1StreamMOTP: Streaming and Unified Framework for Joint 3D Multi-Object Tracking and Trajectory Prediction
3D multi-object tracking and trajectory prediction are two crucial modules in autonomous driving systems. Generally, the two tasks are handled separately in traditional paradigms and a few methods have started to explore…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject Tracking+2BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data
Compared with real-time multi-object tracking (MOT), offline multi-object tracking (OMOT) has the advantages to perform 2D-3D detection fusion, erroneous link correction, and full track optimization but has to deal with …
3D Multi-Object TrackingMulti-Object TrackingMultiple Object TrackingObject+2S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking
3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have shown promising potential for the 3D MOT…
3D Multi-Object TrackingAutonomous DrivingDecoderDenoising+3Track Initialization and Re-Identification for~3D Multi-View Multi-Object Tracking
We propose a 3D multi-object tracking (MOT) solution using only 2D detections from monocular cameras, which automatically initiates/terminates tracks as well as resolves track appearance-reappearance and occlusions. More…
3D Multi-Object TrackingMulti-Object TrackingObjectObject Tracking+1RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud
This paper addresses limitations in 3D tracking-by-detection methods, particularly in identifying legitimate trajectories and reducing state estimation drift in Kalman filters. Existing methods often use threshold-based …
3D Multi-Object TrackingCPUMulti-Object TrackingMultiple Object Tracking+3ADA-Track++: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association
Many query-based approaches for 3D Multi-Object Tracking (MOT) adopt the tracking-by-attention paradigm, utilizing track queries for identity-consistent detection and object queries for identity-agnostic track spawning. …
3D Multi-Object TrackingDecoderMulti-Object TrackingObject TrackingInverse Neural Rendering for Explainable Multi-Object Tracking
Today, most methods for image understanding tasks rely on feed-forward neural networks. While this approach has allowed for empirical accuracy, efficiency, and task adaptation via fine-tuning, it also comes with fundamen…
3D Multi-Object TrackingInverse RenderingMulti-Object TrackingNeural Rendering+2SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system. Despite simplicity and clarity, the performance of end-to-end autonomous driving methods on sub-tasks is still far be…
3D Multi-Object Tracking3D Object DetectionAutonomous Drivingmotion prediction