Results 11 to 20 of about 4,392,511 (325)
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking [PDF]
Computer Vision and Pattern Recognition, 2022 Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that objects move linearly. While this assumption is acceptable for very short periods of occlusion, linear estimates of motion for prolonged time can be highly ...
Jinkun Cao +4 more
semanticscholar +1 more source
TrackFormer: Multi-Object Tracking with Transformers [PDF]
Computer Vision and Pattern Recognition, 2021 The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories.
Tim Meinhardt +3 more
semanticscholar +1 more source
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion [PDF]
Computer Vision and Pattern Recognition, 2021 A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID)for object association.
Pei Sun +6 more
semanticscholar +1 more source
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
Center-based 3D Object Detection and Tracking [PDF]
Computer Vision and Pattern Recognition, 2020 Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges.
Tianwei Yin +2 more
semanticscholar +1 more source
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking [PDF]
Computer Vision and Pattern Recognition, 2023 3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra ...
Yukang Chen +4 more
semanticscholar +1 more source
Perception & Psychophysics, 2007 Is content addressable in the representation that subserves performance in multiple-object-tracking (MOT) experiments? We devised an MOT variant that featured unique, nameable objects (cartoon animals) as stimuli. There were two possible response modes: standard, in which observers were asked to report the locations of all target items, and specific ...
Horowitz, Todd S. +5 more
openaire +3 more sources
Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking [PDF]
European Conference on Computer Vision, 2022 Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture ...
Boyu Chen +8 more
semanticscholar +1 more source
Object Tracking Algorithm with Sparse Prototype Based on TLD [PDF]
Jisuanji gongcheng, 2017 Tracking-learning-detecting(TLD) object tracking algorithm can achieve a long time online tracking.But when the appearance changes of the object by the plane rotation and the object is occluded,the TLD tracking algorithm tracks drift in the process of ...
ZHOU Junna,CHEN Wei,WANG Ke,CAI Changzheng
doaj +1 more source