Learning from evolving video streams in a multi-camera scenario
| LEADER | caa a22 4500 | ||
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| 001 | 605478414 | ||
| 003 | CHVBK | ||
| 005 | 20210128100405.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150901xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10994-015-5515-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-015-5515-y | ||
| 245 | 0 | 0 | |a Learning from evolving video streams in a multi-camera scenario |h [Elektronische Daten] |c [Samaneh Khoshrou, Jaime Cardoso, Luís Teixeira] |
| 520 | 3 | |a Nowadays, video surveillance systems are taking the first steps toward automation, in order to ease the burden on human resources as well as to avoid human error. As the underlying data distribution and the number of concepts change over time, the conventional learning algorithms fail to provide reliable solutions for this setting. In this paper, we formalize a learning concept suitable for multi-camera video surveillance and propose a learning methodology adapted to that new paradigm. The proposed framework resorts to the universal background model to robustly learn individual object models from small samples and to more effectively detect novel classes. The individual models are incrementally updated in an ensemble-based approach, with older models being progressively forgotten. The framework is designed to detect and label new concepts automatically. The system is also designed to exploit active learning strategies, in order to interact wisely with operator, requesting assistance in the most ambiguous to classify observations. The experimental results obtained both on real and synthetic data sets verify the usefulness of the proposed approach. | |
| 540 | |a The Author(s), 2015 | ||
| 690 | 7 | |a Video surveillance |2 nationallicence | |
| 690 | 7 | |a Parallel streams |2 nationallicence | |
| 690 | 7 | |a Active learning |2 nationallicence | |
| 700 | 1 | |a Khoshrou |D Samaneh |u INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal |4 aut | |
| 700 | 1 | |a Cardoso |D Jaime |u INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal |4 aut | |
| 700 | 1 | |a Teixeira |D Luís |u Faculdade de Engenharia da Universidade do Porto (FEUP), Campus da FEUP, Rua Dr. Roberto Frias, n 378, 4200-465, Porto, Portugal |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 609-633 |x 0885-6125 |q 100:2-3<609 |1 2015 |2 100 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-015-5515-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-015-5515-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Khoshrou |D Samaneh |u INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Cardoso |D Jaime |u INESC TEC, Campus da FEUP, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Teixeira |D Luís |u Faculdade de Engenharia da Universidade do Porto (FEUP), Campus da FEUP, Rua Dr. Roberto Frias, n 378, 4200-465, Porto, Portugal |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 100/2-3(2015-09-01), 609-633 |x 0885-6125 |q 100:2-3<609 |1 2015 |2 100 |o 10994 | ||