Paper Title
HUMAN CROWD COUNTING AND ANALYSIS
Article Identifiers
: Authors
Keywords
Clustering, Human Crowd, Human Crowd Computation, Human Detection, OpenCV Library, Unsupervised Clustering, Image Recognition, Object Detection, Video Surveillance, K-Means Clustering.
Abstract
The most accurate human crowd-counting program is what we recommend creating. The software should capture an image that the user manually uploads, a video that the user uploads, or a camera where the population needs to be monitored. The OpenCV library is used to train and create the machine learning algorithm, which counts the number of people in each frame of the input feed. TensorFlow is the developed application that can be used as a standalone application or can be integrated with the other existing applications by exposing an API interface. The method counts the number of people in each frame. Unsupervised clustering is proposed to be developed in this application since it needs less training and is more suited for picture recognition. The developed application can be used as a standalone application or can be integrated with the other existing applications by exposing an API interface.
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How To Cite (APA)
LAASYA SATYA SUNDARI EVANI, KUSUMA KARETI, MANIKYALA SRI LEKHA, MADDIPATI SRI LASYA, & M. SION KUMARI (March-2023). HUMAN CROWD COUNTING AND ANALYSIS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), a799-a803. https://ijnrd.org/papers/IJNRD2303083.pdf
Issue
Volume 8 Issue 3, March-2023
Pages : a799-a803
Other Publication Details
Paper Reg. ID: IJNRD_188332
Published Paper Id: IJNRD2303083
Downloads: 000122254
Research Area: Computer EngineeringÂ
Author Type: Indian Author
Country: Visakhapatnam, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2303083.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303083
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