Multi-camera spatiotemporal deep learning framework for real-time abnormal behavior detection in dense urban environments

Sai Babu Veesam; B. Tarakeswara Rao; Zarina Begum; R. S. M. Lakshmi Patibandla; Arvin Arun Dcosta; Shonak Bansal; Krishna Prakash; Mohammad Rashed Iqbal Faruque; K. S. Al-mugren

doi:10.1038/s41598-025-12388-7

Multi-camera spatiotemporal deep learning framework for real-time abnormal behavior detection in dense urban environments

S Sai Babu Veesam B B. Tarakeswara Rao Z Zarina Begum R R. S. M. Lakshmi Patibandla A Arvin Arun Dcosta S Shonak Bansal K Krishna Prakash M Mohammad Rashed Iqbal Faruque K K. S. Al-mugren

Download PDF View Original

APA MLA Chicago BibTeX RIS

Article Details

Journal Scientific Reports

Volume / Issue Vol. 15, Issue 1

Published July 23, 2025

DOI 10.1038/s41598-025-12388-7

ISSN 2045-2322

Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (9)

S

Sai Babu Veesam

B

B. Tarakeswara Rao

Z

Zarina Begum

R

R. S. M. Lakshmi Patibandla

A

Arvin Arun Dcosta

S

Shonak Bansal

K

Krishna Prakash

M

Mohammad Rashed Iqbal Faruque

K

K. S. Al-mugren

Multi-camera spatiotemporal deep learning framework for real-time abnormal behavior detection in dense urban environments

Article Details

Journal Info

Authors (9)

Related Articles from this Journal

AI models of unstable flow exhibit hallucination

The MCAA-YOLO + XPBI integrated model provides a hybrid intelligent measurement method for predicting the body weight of Hu sheep

Application of iron nano-biofertilizers as a sustainable approach to enhance growth and micronutrient uptake in soybean

Adolescents’ status-dependent public and private conformity to prosocial and antisocial decisions in a public goods game

An explainable hierarchical capsule network framework for supporting expert sensory evaluation of herbal medicines with natural language generation