| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci ICMC, BR-13566590 Sao Carlos, SP - Brazil
Total Affiliations: 1
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| 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) |