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SparseTrack: Multi-Object Tracking by Performing Scene Decomposition Based on Pseudo-Depth
IEEE transactions on circuits and systems for video technology (Print), 2023Exploring robust and efficient association methods has always been an important issue in multi-object tracking (MOT). Although existing tracking methods have achieved impressive performance, congestion and frequent occlusions still pose challenging ...
Zelin Liu +4 more
semanticscholar +1 more source
Multi-Object Tracking Analysis
2021In situ microscopes are capable of imaging the transient dynamics of material processes at the nano-scale spatial resolution. The resulting material images contain the structures of material objects that change over the course of a material process. If one is interested in knowing how a population of material objects is collectively evolved in their ...
Chiwoo Park, Yu Ding
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Multi-object trajectory tracking
Machine Vision and Applications, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Han, Mei +3 more
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UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation
AAAI Conference on Artificial Intelligence, 2023Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes.
Kefu Yi +6 more
semanticscholar +1 more source
Towards Real-Time Multi-Object Tracking
European Conference on Computer Vision, 2019Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association.
Zhongdao Wang +3 more
semanticscholar +1 more source
Evaluating Multi-Object Tracking
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops, 2006Multiple object tracking (MOT) is an active and challenging research topic. Many different approaches to the MOT problem exist, yet there is little agreement amongst the community on how to evaluate or compare these methods, and the amount of literature addressing this problem is limited.
K. Smith +3 more
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Interacting Tracklets for Multi-Object Tracking
IEEE Transactions on Image Processing, 2018In this paper, we propose to exploit the interactions between non-associable tracklets to facilitate multi-object tracking. We introduce two types of tracklet interactions, close interaction and distant interaction. The close interaction imposes physical constraints between two temporally overlapping tracklets and more importantly, allows us to learn ...
Long Lan +5 more
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3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association
IEEE transactions on intelligent transportation systems (Print), 2021This paper proposes a new 3D multi-object tracker to more robustly track objects that are temporarily missed by detectors. Our tracker can better leverage object features for 3D Multi-Object Tracking (MOT) in point clouds.
Hai Wu +4 more
semanticscholar +1 more source
3D Multi-Object Tracking With Adaptive Cubature Kalman Filter for Autonomous Driving
IEEE Transactions on Intelligent Vehicles, 2023A crucial and challenging issue in autonomous driving is dynamic road environment detection and 3D multi object tracking. In this article, we propose a novel framework for online 3D multi-object tracking to eliminate the influence of inherent uncertainty
Ge Guo, Shijie Zhao
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Connected Component Model for Multi-Object Tracking
IEEE Transactions on Image Processing, 2016In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve ...
He, Zhenyu +4 more
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