Multi-Object Tracking and Segmentation With Embedding Mask-Based Affinity Fusion in Hierarchical Data Association

Multi-Object Tracking and Segmentation With Embedding Mask-Based Affinity Fusion in Hierarchical Data Association

In this paper, we propose a highly feasible 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 probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA)...

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Bibliographic Details
Main Authors: Young-Min Song, Young-Chul Yoon, Kwangjin Yoon, Hyunsung Jang, Namkoo Ha, Moongu Jeon
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Multi-object tracking
instance segmentation
tracking by segmentation
online approach
Gaussian mixture probability hypothesis filter
affinity fusion
Online Access:https://ieeexplore.ieee.org/document/9765992/

Internet

https://ieeexplore.ieee.org/document/9765992/

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