Target Detection and Extraction Based on Motion Attention Model

DOI

10.2991/mecae-18.2018.3How to use a DOI?

Keywords

water injection network; water injection network model; forward modeling and inversion iterative algorithm.

Abstract

In the paper, a new motion attention temporal spatial fusion model is constructed for motion object detection and extraction in view of the limitations of target detection and extraction method under global motion scene according to motion attention formation mechanism. In the algorithm, motion vector fields undergo superposition, filtering and other pretreatment firstly. Then, a motion attention fusion model is defined according to temporal - spatial change characteristics of motion vector. The model is adopted for detecting the motion object region. Finally, morphology and boundary following method are utilized for accurate extraction of the object region. The test results of many different global motion video scenes show that the algorithm has better accuracy and real - time performance than other similar algorithms.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Long Liu
AU  - Qing Liu
PY  - 2018/03
DA  - 2018/03
TI  - Target Detection and Extraction Based on Motion Attention Model
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SP  - 9
EP  - 13
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.3
DO  - 10.2991/mecae-18.2018.3
ID  - Liu2018/03
ER  -