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EnglishRecognizing different visual signatures of people across non-overlapping cameras is still an open problem of great interest for the computer vision community, especially due to its importance in automatic video surveillance on large-scale environments. A main aspect of this application field, known as person re-identification (re-id), is the feature extraction step used to define a robust appearance of a person. In this paper, a novel two-branch Convolutional Neural Network (CNN) architecture for person re-id in video sequences is proposed. A pre-trained branch, called Master, leads the learning phase of the other un-trained branch, called Rookie. Using this strategy, the Rookie network is able to learn complementary features with respect to those computed by the Master network, thus obtaining a more discriminative model. Extensive experiments on two popular challenging re-id datasets have shown increasing performance in terms of convergence speed as well as accuracy in comparison to standard models, thus providing an alternative and concrete contribution to the current re-id state-of-the-art.
Master and Rookie Networks for Person Re-identification / Avola, D.; Cascio, M.; Cinque, L.; Fagioli, A.; Foresti, G. L.; Massaroni, C.. - 11679:(2019), pp. 470-479. ((Intervento presentato al convegno 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 tenutosi a ita [10.1007/978-3-030-29891-3_41].
| Titolo: | Master and Rookie Networks for Person Re-identification | |
| Autori: | ||
| Data di pubblicazione: | 2019 | |
| Serie: | ||
| Citazione: | Master and Rookie Networks for Person Re-identification / Avola, D.; Cascio, M.; Cinque, L.; Fagioli, A.; Foresti, G. L.; Massaroni, C.. - 11679:(2019), pp. 470-479. ((Intervento presentato al convegno 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 tenutosi a ita [10.1007/978-3-030-29891-3_41]. | |
| Handle: | http://hdl.handle.net/11573/1350656 | |
| Appartiene alla tipologia: | 04b Atto di convegno in volume |
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