UGC Approved Journal no 63975(19)

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Published in:

Volume 8 Issue 1
January-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2101273


Registration ID:
305482

Page Number

493-496

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Title

INSTANCE SEGMENTATION OF VIDEO USING MASK R-CNN

Abstract

Object detection in the digital images, videos plays an important role in the real world. At present object detection is done in the images with the help of the models like Fast RCNN. In the object detection the objects in the image are identified with the help of bounding boxes around objects. The object detection is done by generating the bounding box around the object with (x,y) coordinates but with this we cannot identify which pixel belongs to the background and which pixel belongs to the foreground. Image classification only categorizes the objects in the input image. Object detection localizes every object in the input image. Whereas the semantic segmentation can classify all the similar objects as a single instance, the main drawback of this is we cannot identify how many similar objects are present in the single instance. In semantic segmentation we can only segment objects by using bounding boxes and cannot identify individual instances of even same classes. For each instance of an object in an image the model generates bounding boxes and segmentation masks. In the Instance segmentation objects of same class will assign a different instance. Here we can compute a mask (pixel level) for each and every object in the input image this can be achieved with the help of Mask R-CNN In this paper the Mask R-CNN is proposed to add the mask feature to every object at pixel level along with the bounded box. The Mask R-CNN can perform the instance segmentation along with the object detection. It outperforms all the other previous models.

Key Words

Object detection, R-CNN, region proposal network, instance segmentation

Cite This Article

"INSTANCE SEGMENTATION OF VIDEO USING MASK R-CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 1, page no.493-496, January-2021, Available :http://www.jetir.org/papers/JETIR2101273.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"INSTANCE SEGMENTATION OF VIDEO USING MASK R-CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 1, page no. pp493-496, January-2021, Available at : http://www.jetir.org/papers/JETIR2101273.pdf

Publication Details

Published Paper ID: JETIR2101273
Registration ID: 305482
Published In: Volume 8 | Issue 1 | Year January-2021
DOI (Digital Object Identifier):
Page No: 493-496
Country: Siddipet, Telangana, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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