Supervised deep learning approaches for anomaly detection and recognition in crowd scenes
Joshi, Kinjal (Charutar Vidya Mandal University (Índia))
Patel, Narendra (Birla Vishvakarma Mahavidyalaya (Índia))

Data:	2025
Resum:	These days consciousness about public safety increases and CCTV cameras are installed at almost all public places. But generally automatic smart surveillance systems are not available. In this manuscript, emphasis is given to detect and classify abnormal events in surveillance video especially in crowd environments. Abnormal event detection is a challenging task because the definition of abnormality is subjective. A normal event in one situation can be considered an abnormal event in another case. In the surveillance video with a dense crowd, automatic anomaly detection becomes very difficult because of clutter and severe occlusion. This manuscript represents CNN (Convolutional Neural Network) and CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) based approaches for detection and classification of abnormal events. The CNN architecture is developed from scratch and can be used for spatial domains. LSTM architecture is developed for the temporal domain. Feature sequences are generated using CNN model and given as input to LSTM model. Experiments are carried out using five different publicly available benchmark datasets. The performance is measured by accuracy and area under the ROC (receiver operating characteristic) curve (AUC). CNN-LSTM approach works better than only CNN.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Abnormal event ; Classification ; CNN ; LSTM ; Abnormal event detection
Publicat a:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 1 (2025) , p. 31-50 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1631
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000001040
DOI: 10.5565/rev/elcvia.2412025
DOI: 10.5565/rev/elcvia.1631

20 p, 1.0 MB

El registre apareix a les col·leccions:
Articles > Articles publicats > ELCVIA
Articles > Articles de recerca