Results 31 to 40 of about 711,042 (336)
Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking [PDF]
AAAI Conference on Artificial Intelligence, 2023 Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance ...
Ming-Hsuan Yang +6 more
semanticscholar +1 more source
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes [PDF]
IEEE International Conference on Computer Vision, 2023 Multi-object tracking (MOT) in sports scenes plays a critical role in gathering players statistics, supporting further applications, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on this domain.
Yutao Cui +5 more
semanticscholar +1 more source
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors [PDF]
Computer Vision and Pattern Recognition, 2022 In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, e.g.
Yuang Zhang, Tiancai Wang, Xiangyu Zhang
semanticscholar +1 more source
Multi-Object Tracking with Tracked Object Bounding Box Association [PDF]
2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2021 6 pages, accepted paper at ICME workshop ...
Yang, Nanyang, Wang, Yi, Chau, Lap-Pui
openaire +2 more sources
Survey of Deep Online Multi-object Tracking Algorithms [PDF]
Jisuanji kexue yu tansuo, 2022 Video multi-object tracking is a key task in the field of computer vision and has a wide application prospect in industry, commerce and military fields.
LIU Wenqiang, QIU Hangping, LI Hang, YANG Li, LI Yang, MIAO Zhuang, LI Yi, ZHAO Xinxin
doaj +1 more source
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking [PDF]
IEEE International Conference on Computer Vision, 2023 As a video task, Multiple Object Tracking (MOT) is expected to capture temporal information of targets effectively. Unfortunately, most existing methods only explicitly exploit the object features between adjacent frames, while lacking the capacity to ...
Ruopeng Gao, Limin Wang
semanticscholar +1 more source
MeMOT: Multi-Object Tracking with Memory [PDF]
Computer Vision and Pattern Recognition, 2022 We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span.
Jiarui Cai +6 more
semanticscholar +1 more source
MotionTrack: Learning Robust Short-Term and Long-Term Motions for Multi-Object Tracking [PDF]
Computer Vision and Pattern Recognition, 2023 The main challenge of Multi-Object Tracking (MOT) lies in maintaining a continuous trajectory for each target. Existing methods often learn reliable motion patterns to match the same target between adjacent frames and discriminative appearance features ...
Zheng Qin +5 more
semanticscholar +1 more source
Fast Timeline Based Multi Object Online Tracking
Transport and Telecommunication, 2023 Fast state-of-the-art multi-object-tracking (MOT) schemes, such as reported in challenges MOT16 and Mot20, perform tracking on a single sensor, often couple tracking and detection, support only one kind of object representation or don’t take varying ...
Hünermund Martin +2 more
doaj +1 more source
MOTS: Multi-Object Tracking and Segmentation [PDF]
Computer Vision and Pattern Recognition, 2019 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-automatic annotation procedure.
P. Voigtlaender +6 more
semanticscholar +1 more source