Generalization of feature embeddings transferred from different video anomaly detection domains

Autor(es)

dos Santos, Fernando P. ^[1] ; Ribeiro, Leonardo S. F. ^[1] ; Ponti, Moacir A. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci ICMC, BR-13566590 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION; v. 60, p. 407-416, APR 2019.
• Citações Web of Science: 3

Resumo

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)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 18/22482-0 - Aprendendo características de conteúdo visual sob condições de supervisão limitada utilizando múltiplos domínios
• Beneficiário: Moacir Antonelli Ponti
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 17/22366-8 - Redes geradoras e aprendizado de características para busca entre domínios visuais
• Beneficiário: Leo Sampaio Ferraz Ribeiro
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto