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Motion target detection algorithm based on monocular vision

Authors:

Yuxi Feng,

Qizhou Wu,

Guotian HeAuthors Info & Claims

ICSCA '17: Proceedings of the 6th International Conference on Software and Computer Applications

Pages 107 - 111

https://doi.org/10.1145/3056662.3056669

Published: 26 February 2017 Publication History

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Abstract

In this paper, presents an improved motion obstacle detection algorithm based on monocular vision by contrast analysis of optical flow method, frame difference method and background difference method. Firstly, the image is collected by the monocular camera, preprocessed, and then processed by an improved motion obstacle detection algorithm. Finally, the image is processed to solve the void problem and get the complete motion obstacle information. The experimental results show that the method proposed in this paper requires less computational complexity and simpler parameter design than the traditional detection algorithm, and the motion detection algorithm proposed in this paper can satisfy the real-time and real-time Accuracy requirements.

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Wang HChen KLi Y(2023)Automatic Detection Method for Black Smoke Vehicles Considering Motion ShadowsSensors10.3390/s2319828123:19(8281)Online publication date: 6-Oct-2023
https://doi.org/10.3390/s23198281
Chen YGuan YHan T(2020)Distributed Video Analysis for Mobile Live Broadcasting Services2020 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC45663.2020.9120783(1-6)Online publication date: May-2020
https://doi.org/10.1109/WCNC45663.2020.9120783

Index Terms

Motion target detection algorithm based on monocular vision
1. Theory of computation
  1. Design and analysis of algorithms

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Published In

ICSCA '17: Proceedings of the 6th International Conference on Software and Computer Applications

February 2017

339 pages

ISBN:9781450348577

DOI:10.1145/3056662

Conference Chairs:
Vitaliy Mezhuyev
Universiti Malaysia Pahang, Malaysia
,
Luigi Benedicenti
University of Regina, Canada

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Association for Computing Machinery

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Published: 26 February 2017

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ICSCA 2017: 2017 6th International Conference on Software and Computer Applications

February 26 - 28, 2017

Bangkok, Thailand

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Wang HChen KLi Y(2023)Automatic Detection Method for Black Smoke Vehicles Considering Motion ShadowsSensors10.3390/s2319828123:19(8281)Online publication date: 6-Oct-2023
https://doi.org/10.3390/s23198281
Chen YGuan YHan T(2020)Distributed Video Analysis for Mobile Live Broadcasting Services2020 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC45663.2020.9120783(1-6)Online publication date: May-2020
https://doi.org/10.1109/WCNC45663.2020.9120783

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Local-to-global background modeling for moving object detection from non-static cameras

Vision-Based Detection and Tracking of Moving Target in Video Surveillance

Indoor robot motion based on monocular images

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Local-to-global background modeling for moving object detection from non-static cameras

Vision-Based Detection and Tracking of Moving Target in Video Surveillance

Indoor robot motion based on monocular images

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