Papers Multi-Object Tracking and Segmentation
“Multi-Object Tracking and Segmentation” 태그가 달린 논문 30편 · 필터 해제
Seg2Track++: Probabilistic Track Validation and Data Association for Multi-Object Tracking and Segmentation
Autonomous systems require robust Multi-Object Tracking and Segmentation (MOTS) to operate reliably in dynamic environments, ensuring consistent object identities and precise mask-level delineation. Foundation models suc…
Multi-Object Tracking and SegmentationZero-shot GeneralizationInstance SegmentationSeg2Track-SAM2: SAM2-based Multi-object Tracking and Segmentation
Autonomous-driving perception systems require robust Multi-Object Tracking (MOT) to operate reliably in dynamic environments. MOT maintains consistent object identities across frames while preserving spatial accuracy. Re…
Multi-Object Tracking and SegmentationVideo SegmentationTemporal-Enhanced Multimodal Transformer for Referring Multi-Object Tracking and Segmentation
Referring multi-object tracking (RMOT) is an emerging cross-modal task that aims to locate an arbitrary number of target objects and maintain their identities referred by a language expression in a video. This intricate …
Multi-Object TrackingMulti-Object Tracking and SegmentationObject TrackingReferring Multi-Object Tracking+3CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation
The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims to track and segment multiple objects in video frames, h…
Autonomous DrivingInstance SegmentationMulti-Object TrackingMulti-Object Tracking and Segmentation+7Deep Learning Techniques for Video Instance Segmentation: A Survey
Video instance segmentation, also known as multi-object tracking and segmentation, is an emerging computer vision research area introduced in 2019, aiming at detecting, segmenting, and tracking instances in videos simult…
Action RecognitionDeep LearningInstance SegmentationMulti-Object Tracking+7Universal Instance Perception as Object Discovery and Retrieval
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independen…
Described Object DetectionGeneralized Referring Expression ComprehensionInstance SegmentationMulti-Object Tracking and Segmentation+16Context-Aware Relative Object Queries To Unify Video Instance and Panoptic Segmentation
Object queries have emerged as a powerful abstraction to generically represent object proposals. However, their use for temporal tasks like video segmentation poses two questions: 1) How to process frames sequentiall…
Instance SegmentationMulti-Object TrackingMulti-Object Tracking and SegmentationObject+9Track Targets by Dense Spatio-Temporal Position Encoding
In this work, we propose a novel paradigm to encode the position of targets for target tracking in videos using transformers. The proposed paradigm, Dense Spatio-Temporal (DST) position encoding, encodes spatio-temporal …
Multi-Object TrackingMulti-Object Tracking and SegmentationObjectObject Tracking+1BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video
Multiple existing benchmarks involve tracking and segmenting objects in video e.g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is little interaction between them due to t…
Long-tail Video Object SegmentationMulti-Object TrackingMulti-Object Tracking and SegmentationObject+6Multi-Object Tracking and Segmentation via Neural Message Passing
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) and Multiple Object Tracking and Segmentation (MOTS) within the tracking-by-detection paradigm. However, they also introduce a major challenge for le…
Multi-Object TrackingMulti-Object Tracking and SegmentationMultiple Object TrackingObject Tracking+1Towards Grand Unification of Object Tracking
We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. Due to the fragmented definitions of the obje…
Multi-Object TrackingMulti-Object Tracking and SegmentationMultiple Object TrackingObject+3D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
Despite receiving significant attention from the research community, the task of segmenting and tracking objects in monocular videos still has much room for improvement. Existing works have simultaneously justified the e…
Multi-Object Tracking and SegmentationSegmentationSemantic SegmentationUnsupervised Video Object Segmentation+4PolyTrack: Tracking with Bounding Polygons
In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segmentation using bounding polygons. Polytrack detects objects by producing heatmaps of their center keypoint. For each of the…
Multi-Object TrackingMulti-Object Tracking and SegmentationObject TrackingSegmentationMulti-Object Tracking and Segmentation with a Space-Time Memory Network
We propose a method for multi-object tracking and segmentation based on a novel memory-based mechanism to associate tracklets. The proposed tracker, MeNToS, addresses particularly the long-term data association problem, …
Instance SegmentationMulti-Object TrackingMulti-Object Tracking and SegmentationMultiple Object Tracking+9DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception
Multi-object tracking and segmentation (MOTS) is a critical task for autonomous driving applications. The existing MOTS studies face two critical challenges: 1) the published datasets inadequately capture the real-world …
Autonomous DrivingDiversityMulti-Object TrackingMulti-Object Tracking and Segmentation+1MeNToS: Tracklets Association with a Space-Time Memory Network
We propose a method for multi-object tracking and segmentation (MOTS) that does not require fine-tuning or per benchmark hyperparameter selection. The proposed method addresses particularly the data association problem. …
Instance SegmentationMulti-Object TrackingMulti-Object Tracking and SegmentationObject+6Do Different Tracking Tasks Require Different Appearance Models?
Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem i…
Multi-Object TrackingMulti-Object Tracking and SegmentationMultiple People TrackingObject Tracking+10Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. Most approaches only exploit the temporal dimension to address the association problem, …
Multi-Object Tracking and SegmentationMultiple Object Track and SegmentationMultiple Object TrackingObject+3EagerMOT: 3D Multi-Object Tracking via Sensor Fusion
Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time. Existing methods rely on depth sensors (e.g., LiDAR) to de…
3D Multi-Object TrackingMotion PlanningMulti-Object TrackingMulti-Object Tracking and Segmentation+4MOTS R-CNN: Cosine-margin-triplet loss for multi-object tracking
One of the central tasks of multi-object tracking involves learning a distance metric that is consistent with the semantic similarities of objects. The design of an appropriate loss function that encourages discriminativ…
Metric LearningMulti-Object TrackingMulti-Object Tracking and SegmentationObject Tracking+1