UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 7
July-2021
eISSN: 2349-5162

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

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR2107439


Registration ID:
312626

Page Number

d379-d383

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Title

Real-time Obstacle Detection and Object Tracking using Machine Learning Techniques

Abstract

Identify the real-time obstacle that plays a key role in everyday life. With the use of a camera, we attempted to identify many obstacles in a single frame in this paper. In this paper, we use classifiers to classifies objects: K-nearest neighbor (KNN), random forest, and decision tree. Determine if the obstacle is static or dynamic using the above classifiers, then compare the results, and also if an obstacle is dynamic then object tracking process can be done.

Key Words

K-nearest neighbor, Random Forest, Decision tree, CNN.

Cite This Article

"Real-time Obstacle Detection and Object Tracking using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.d379-d383, July-2021, Available :http://www.jetir.org/papers/JETIR2107439.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

"Real-time Obstacle Detection and Object Tracking using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppd379-d383, July-2021, Available at : http://www.jetir.org/papers/JETIR2107439.pdf

Publication Details

Published Paper ID: JETIR2107439
Registration ID: 312626
Published In: Volume 8 | Issue 7 | Year July-2021
DOI (Digital Object Identifier):
Page No: d379-d383
Country: Mandya , Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162


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