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

• Type: Article in International Scientific Journal
• Date: 2021-11

Authors

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

Journal

Title: Sensors

Vol. 21 No. 22

Pages: 1-12

ISSN: 1424-3210

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus - 0 Citations

Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Associated Projects

Safe Cities - Safe Cities - Inovação para Construir Cidades Seguras

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12