Weakly supervised Video Anomaly Detection based on 3D Convolution and LSTM

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021-11

Autores

Zhen Ma (Autor) FEUP; José J. M. Machado (Autor) FEUP; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Sensors

Vol. 21 Nº 22

Páginas: 1-12

ISSN: 1424-3210

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 0 Citações

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências da engenharia e tecnologias

Projetos Associados

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Outras Informações

ID Authenticus: P-00V-P02

DOI: 10.3390/s21227508

Abstract (EN): Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12