Papers Multi-Object Tracking
“Multi-Object Tracking” 태그가 달린 논문 812편 · 필터 해제
CAMOT: Camera Angle-aware Multi-Object Tracking
This paper proposes CAMOT, a simple camera angle estimator for multi-object tracking to tackle two problems: 1) occlusion and 2) inaccurate distance estimation in the depth direction. Under the assumption that multiple o…
Multi-Object TrackingObjectobject-detectionObject Detection+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+1Masks and Boxes: Combining the Best of Both Worlds for Multi-Object Tracking
Multi-object tracking (MOT) involves identifying and consistently tracking objects across video sequences. Traditional tracking-by-detection methods, while effective, often require extensive tuning and lack generalizabil…
Hand SegmentationManagementMulti-Object TrackingObject 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+4Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown
Multi-object tracking (MOT) emerges as a pivotal and highly promising branch in the field of computer vision. Classical closed-vocabulary MOT (CV-MOT) methods aim to track objects of predefined categories. Recently, some…
Multi-Object TrackingMultiple Object TrackingObjectObject Tracking+1When to Extract ReID Features: A Selective Approach for Improved Multiple Object Tracking
Extracting and matching Re-Identification (ReID) features is used by many state-of-the-art (SOTA) Multiple Object Tracking (MOT) methods, particularly effective against frequent and long-term occlusions. While end-to-end…
Multi-Object TrackingMultiple Object Trackingobject-detectionObject Detection+1RCBEVDet++: 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+5LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID Feature Integration
The Lightweight Integrated Tracking-Feature Extraction (LITE) paradigm is introduced as a novel multi-object tracking (MOT) approach. It enhances ReID-based trackers by eliminating inference, pre-processing, post-process…
Multi-Object TrackingObject TrackingGr-IoU: Ground-Intersection over Union for Robust Multi-Object Tracking with 3D Geometric Constraints
We propose a Ground IoU (Gr-IoU) to address the data association problem in multi-object tracking. When tracking objects detected by a camera, it often occurs that the same object is assigned different IDs in consecutive…
Multi-Object TrackingObject TrackingOne Homography is All You Need: IMM-based Joint Homography and Multiple Object State Estimation
A novel online MOT algorithm, IMM Joint Homography State Estimation (IMM-JHSE), is proposed. IMM-JHSE uses an initial homography estimate as the only additional 3D information, whereas other 3D MOT methods use regular 3D…
AllMotion CompensationMulti-Object Tracking+3TP-GMOT: Tracking Generic Multiple Object by Textual Prompt with Motion-Appearance Cost (MAC) SORT
While Multi-Object Tracking (MOT) has made substantial advancements, it is limited by heavy reliance on prior knowledge and limited to predefined categories. In contrast, Generic Multiple Object Tracking (GMOT), tracking…
Grounded Multiple Object TrackingMulti-Object TrackingMultiple Object TrackingObject+3YOLOO: 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 TrackingTrackSSM: A General Motion Predictor by State-Space Model
Temporal motion modeling has always been a key component in multiple object tracking (MOT) which can ensure smooth trajectory movement and provide accurate positional information to enhance association precision. However…
DecoderMambaMulti-Object TrackingMultiple Object Tracking+4UTrack: Multi-Object Tracking with Uncertain Detections
The tracking-by-detection paradigm is the mainstream in multi-object tracking, associating tracks to the predictions of an object detector. Although exhibiting uncertainty through a confidence score, these predictions do…
Autonomous DrivingMulti-Object TrackingObjectobject-detection+2ConsistencyTrack: A Robust Multi-Object Tracker with a Generation Strategy of Consistency Model
Multi-object tracking (MOT) is a critical technology in computer vision, designed to detect multiple targets in video sequences and assign each target a unique ID per frame. Existed MOT methods excel at accurately tracki…
DenoisingMulti-Object TrackingObject TrackingDecentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes
This paper tackles the challenge of multi-sensor multi-object tracking by proposing various decentralised Variational Inference (VI) schemes that match the tracking performance of centralised sensor fusion with only loca…
Multi-Object TrackingObjectObject TrackingSensor Fusion+1BoostTrack++: using tracklet information to detect more objects in multiple object tracking
Multiple object tracking (MOT) depends heavily on selection of true positive detected bounding boxes. However, this aspect of the problem is mostly overlooked or mitigated by employing two-stage association and utilizing…
Multi-Object TrackingMultiple Object TrackingObject TrackingMCTR: Multi Camera Tracking Transformer
Multi-camera tracking plays a pivotal role in various real-world applications. While end-to-end methods have gained significant interest in single-camera tracking, multi-camera tracking remains predominantly reliant on h…
Multi-Object TrackingObjectobject-detectionObject Detection+1BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks
Multi-object tracking (MOT) aims to construct moving trajectories for objects, and modern multi-object trackers mainly utilize the tracking-by-detection methodology. Initial approaches to MOT attacks primarily aimed to d…
Adversarial AttackMulti-Object TrackingObjectObject TrackingMambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model
Tracking by detection has been the prevailing paradigm in the field of Multi-object Tracking (MOT). These methods typically rely on the Kalman Filter to estimate the future locations of objects, assuming linear object mo…
Mambamotion predictionMulti-Object TrackingMultiple Object Tracking+2