Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification
The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially u...
Main Authors: | H. Y. Swathi, G. Shivakumar |
---|---|
Format: | Article |
Language: | English |
Published: |
AIMS Press
2023-05-01
|
Series: | Mathematical Biosciences and Engineering |
Subjects: | |
Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023558?viewType=HTML |
Similar Items
-
AVMSN: An Audio-Visual Two Stream Crowd Counting Framework Under Low-Quality Conditions
by: Ruihan Hu, et al.
Published: (2021-01-01) -
Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis
by: Zeinab Ebrahimpour, et al.
Published: (2019-10-01) -
ASTIR: Spatio-Temporal Data Mining for Crowd Flow Prediction
by: Lablack Mourad, et al.
Published: (2019-01-01) -
Modal Intonation Features of Crowd Scenes in the Operetta “O olmasyn, bu olsun” (“Not that, so this”) by U. Hajibeyli
by: Наїба Шахмамедова
Published: (2021-06-01) -
Density aware anomaly detection in crowded scenes
by: Ayse Elvan Gunduz, et al.
Published: (2016-08-01)