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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Generalization of feature embeddings transferred from different video anomaly detection domains

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Author(s):
dos Santos, Fernando P. [1] ; Ribeiro, Leonardo S. F. [1] ; Ponti, Moacir A. [1]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci ICMC, BR-13566590 Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION; v. 60, p. 407-416, APR 2019.
Web of Science Citations: 3
Abstract

Detecting anomalous activity in video surveillance often suffers from limited availability of training data. Transfer learning may close this gap, allowing to use existing annotated data from some source domain. However, analyzing the source feature space in terms of its potential for transfer of learning to another context is still to be investigated. This paper reports a study on video anomaly detection, focusing on the analysis of feature embeddings of pre-trained CNNs with the use of novel cross-domain generalization measures that allow to study how source features generalize for different target video domains. This generalization analysis represents not only a theoretical approach, can be useful in practice as a path to understand which datasets allow better transfer of knowledge. Our results confirm this, achieving better anomaly detectors for video frames and allowing analysis of transfer learning's positive and negative aspects. (C) 2019 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 18/22482-0 - Learning features from visual content under limited supervision using multiple domains
Grantee:Moacir Antonelli Ponti
Support Opportunities: Regular Research Grants
FAPESP's process: 17/22366-8 - Generative networks and feature learning for cross domain visual search
Grantee:Leo Sampaio Ferraz Ribeiro
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)