Papers Object Tracking
“Object Tracking” 태그가 달린 논문 2,087편 · 필터 해제
Associate 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+1FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking
Multiple object tracking (MOT) involves identifying multiple targets and assigning them corresponding IDs within a video sequence, where occlusions are often encountered. Recent methods address occlusions using appearanc…
Continual LearningMultiple Object TrackingObject TrackingWhen 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+1A Low-Computational Video Synopsis Framework with a Standard Dataset
Video synopsis is an efficient method for condensing surveillance videos. This technique begins with the detection and tracking of objects, followed by the creation of object tubes. These tubes consist of sequences, each…
Objectobject-detectionObject DetectionObject Tracking+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 TrackingTP-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+2Mismatched: Evaluating the Limits of Image Matching Approaches and Benchmarks
Three-dimensional (3D) reconstruction from two-dimensional images is an active research field in computer vision, with applications ranging from navigation and object tracking to segmentation and three-dimensional modeli…
3D ReconstructionEdge DetectionObject TrackingEstimating Dynamic Flow Features in Groups of Tracked Objects
Interpreting motion captured in image sequences is crucial for a wide range of computer vision applications. Typical estimation approaches include optical flow (OF), which approximates the apparent motion instantaneously…
Multiple Object TrackingObject TrackingOptical Flow EstimationConsistencyTrack: 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 TrackingCamouflaged Object Tracking: A Benchmark
Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress…
ObjectObject TrackingVisual 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+1BihoT: A Large-Scale Dataset and Benchmark for Hyperspectral Camouflaged Object Tracking
Hyperspectral object tracking (HOT) has exhibited potential in various applications, particularly in scenes where objects are camouflaged. Existing trackers can effectively retrieve objects via band regrouping because of…
Object TrackingBankTweak: 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 Tracking