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Tracklet association based multi-target tracking
Multimedia Tools and Applications, 2016This paper proposes a novel multi-target tracking framework, where two different association strategies are utilized to obtain local and global tracking trajectories. Specifically, a scene self-adaptive model is first utilized to generate local trajectories by constructing the association between detection responses and tracking tracklets; then, a ...
Songhao Zhu, Zhe Shi, Chengjian Sun
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Signal Processing, 2016 A new sensor-selection solution within a multi-Bernoulli-based multi-target tracking framework is presented. The proposed method is especially designed for the general multi-target tracking case with no prior knowledge of the clutter distribution or the ...
Amirali Khodadadian Göstar +2 more
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Co-operative Multi-target Tracking and Classification
2004This paper describes a real-time system for multi-target tracking and classification in image sequences from a single stationary camera. Several targets can be tracked simultaneously in spite of splits and merges amongst the foreground objects and presence of clutter in the segmentation results.
Kumar, P. +3 more
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An association-based multi-target tracking method
Proceedings of the 2013 Research in Adaptive and Convergent Systems, 2013Multi-target tracking is widely studied, but it is still a difficult problem due to occlusion or split/merge of target images in reality. Solving such problems is the key in multi-target tracking. In this paper, we propose a modified particle filter method to track multiple targets, based on improved target detection and data association.
Ai Min Li, Pil Seong Park
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A particle filter approach for multi-target tracking
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007The problem of tracking multiple objects poses a number of challenges due to the ambiguity of the observations and the presence of partial or complete occlusions. This paper introduces a novel extension to the Particle Filter algorithm for tracking multiple objects with a vision system.
Hwang Ryol Ryu, Manfred Huber
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2013
Multi-target tracking refers to sequential estimation of the number of targets and their states (positions, velocities, etc.) tagged by a unique label. Hence the output of a tracking algorithm are tracks, where a track represents a labeled temporal sequence of state estimates, associated with the same target.
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Multi-target tracking refers to sequential estimation of the number of targets and their states (positions, velocities, etc.) tagged by a unique label. Hence the output of a tracking algorithm are tracks, where a track represents a labeled temporal sequence of state estimates, associated with the same target.
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Track Management for Distributed Multi-target Tracking in Sensor Network
2019This paper addresses track management for multiple target tracking (MTT) with a sensor network. Track management is needed for track generation and extinction when the targets set is unknown. Based on a consensus-based fusion algorithm, we develope a MTT algorithm that includes the measurement-to-track association (M2TA) and track management. It can be
Woo-Cheol Lee, Han-Lim Choi
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A hierarchical feature model for multi-target tracking
2017 IEEE International Conference on Image Processing (ICIP), 2017We propose a novel Hierarchical Feature Model (HFM) for multi-target tracking. The traditional tracking algorithms use handcrafted features that cannot track targets accurately when the target model changes due to articulation, illumination intensity variation or perspective distortions. Our HFM explore deep features to model the appearance of targets.
Mohib Ullah +3 more
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Track Coalescence Avoidance in Multi-target Tracking
2023 4th International Conference on Advancements in Computational Sciences (ICACS), 2023Sufyan Ali Memon +2 more
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A Detection-Aided Multi-target Tracking Algorithm
2010 International Conference on Machine Vision and Human-machine Interface, 2010This paper addresses the problem of tracking multiple objects in monocular video sequences. This problem is difficult because one needs to identify the targets by the finite measurement data, which may be affected by variations of pose, environment clutters, etc.
Jianguo Lu, Anni Cai, Lili Li
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