Recognizing 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.
CITATION STYLE
Avola, D., Cascio, M., Cinque, L., Fagioli, A., Foresti, G. L., & Massaroni, C. (2019). Master and Rookie Networks for Person Re-identification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11679 LNCS, pp. 470–479). Springer Verlag. https://doi.org/10.1007/978-3-030-29891-3_41
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