Papers Multi-Object Tracking and Segmentation
“Multi-Object Tracking and Segmentation” 태그가 달린 논문 30편 · 필터 해제
Weakly Supervised Multi-Object Tracking and Segmentation
We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation. …
Instance SegmentationMulti-Object TrackingMulti-Object Tracking and SegmentationMulti-Task Learning+6Continuous Copy-Paste for One-Stage Multi-Object Tracking and Segmentation
Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods. By separating the instance segmentation stage from the tracking stage, two-step methods can exploit non-video…
Data AugmentationInstance SegmentationMulti-Object TrackingMulti-Object Tracking and Segmentation+3Assignment-Space-Based Multi-Object Tracking and Segmentation
Multi-object tracking and segmentation (MOTS) is important for understanding dynamic scenes in video data. Existing methods perform well on multi-object detection and segmentation for independent video frames, but tr…
Multi-Object TrackingMulti-Object Tracking and SegmentationObjectobject-detection+3Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion
In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture pro…
CPUInstance SegmentationMulti-Object TrackingMulti-Object Tracking and Segmentation+2ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation
We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement. However, it remains challenging for refining MOTS results, which could be attributed to that appearance features are no…
Multi-Object TrackingMulti-Object Tracking and SegmentationMultiple Object TrackingObject+1Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt convolutions for feature extraction. However, as affected by the inherent receptive field, convolution bas…
Multi-Object TrackingMulti-Object Tracking and SegmentationObject TrackingOnline Multi-Object TrackingPointTrack++ for Effective Online Multi-Object Tracking and Segmentation
Multiple-object tracking and segmentation (MOTS) is a novel computer vision task that aims to jointly perform multiple object tracking (MOT) and instance segmentation. In this work, we present PointTrack++, an effective …
Data AugmentationDecoderInstance SegmentationMulti-Object Tracking+7Learning Multi-Object Tracking and Segmentation from Automatic Annotations
In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns r…
Instance SegmentationMulti-Object TrackingMulti-Object Tracking and SegmentationObject+3MOTS: Multi-Object Tracking and Segmentation
This paper extends the popular task of multi-object tracking to multi-object tracking and segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two existing tracking datasets using a semi-au…
Multi-Object TrackingMulti-Object Tracking and SegmentationMultiple Object TrackingObject+2BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to stu…
Autonomous DrivingDiversityDomain AdaptationDrivable Area Detection+8