Extracting Moving Objects More Accurately: A CDA Contour Optimizer

In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing...

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Vydané v:IEEE transactions on circuits and systems for video technology Ročník 31; číslo 12; s. 4840 - 4849
Hlavní autori: Gao, Fei, Li, Yunyang, Lu, Shufang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Change detection
change detection algorithm
Change detection algorithms
Contours
Edge detection
Feature extraction
Image edge detection
motion detection
Moving object recognition
Optical filters
Optimization
optimization method
Post-processing algorithm
Semantics
Supervised learning
Unsupervised learning
ISSN:1051-8215, 1558-2205
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Popis
Shrnutí:In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in ChangeDetection.net are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://github.com/walty19950301/CDA-contour-optimizer .
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2021.3055539