Filter Learning from Deep Descriptors of a Fully Convolutional Siamese Network for Tracking in Videos

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Chaves, Hugo de Lima ; Ribeiro, Kevyn Swhants ; Brito, Andre de Souza ; Tacon, Hemerson ; Vieira, Marcelo Bernardes ; Cerqueira, Augusto Santiago ; Villela, Saulo Moraes ; Maia, Helena de Almeida ; Concha, Darwin Ttito ; Pedrini, Helio ; Farinella, GM ; Radeva, P ; Braz, J

• Tipo de documento: Artigo Científico
• Fonte: VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP; v. N/A, p. 10-pg., 2020-01-01.

Resumo

Siamese Neural Networks (SNNs) attracted the attention of the Visual Object Tracking community due to their relatively low computational cost and high efficacy to compare similarity between a reference and a candidate object to track its trajectory in a video over time. However, a video tracker that purely relies on an SNN might suffer from drifting due to changes in the target object. We propose a framework to take into account the changes of the target object in multiple time-based descriptors. In order to show its validity, we define long-term and short-term descriptors based on the first and the recent appearance of the object, respectively. Such memories are combined into a final descriptor that is the actual tracking reference. To compute the short-term memory descriptor, we estimate a filter bank through the usage of a genetic algorithm strategy. The final method has a low computational cost since it is applied through convolution operations along the tracking. According to the experiments performed in the widely used OTB50 dataset, our proposal improves the performance of an SNN dedicated to visual object tracking, being comparable to the state of the art methods. (AU)

• Processo FAPESP: 17/09160-1 - Reconhecimento de Ações Humanas em Vídeos
• Beneficiário: Helena de Almeida Maia
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
• Beneficiário: Anderson de Rezende Rocha
• Modalidade de apoio: Auxílio à Pesquisa - Temático