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dc.contributor.authorJiang C
dc.contributor.authorXiao J
dc.contributor.authorXie Y
dc.contributor.authorTillo T
dc.contributor.authorHuang K
dc.date.accessioned2018-08-03T13:40:48Z
dc.date.available2018-08-03T13:40:48Z
dc.date.issued2018
dc.identifier.issn0925-2312
dc.identifier.urihttp://dx.doi.org/10.1016/j.neucom.2017.10.043
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0925231217316946
dc.identifier.urihttp://hdl.handle.net/10863/5553
dc.description.abstractVisual object tracking is a challenging task considering illumination variation, occlusion, rotation, deformation and other problems. In this paper, we extend a Siamese INstance search Tracker (SINT) with model updating mechanism to improve its tracking robustness. SINT uses convolutional neural network (CNN) features, and compares the new frame features with the target features in the first frame. The candidate region with the highest similarity score is considered as the tracking result. However, SINT is not robust against large target variation because the matching model is not updated during the whole tracking process. To combat this defect, we propose an Ensemble Siamese Tracker (EST), where the final similarity score is also affected by the similarity with tracking results in recent frames instead of solely considering the first frame. Tracking results in recent frames are used to adjust the model for continuous target change. Meanwhile, we combine large displacement optical flow method with EST to further improve the performance (called EST+). We test the proposed EST and EST+ on a standard tracking benchmark OTB. It turns out the average overlap ratio of EST and EST+ increase 2.72% and 3.55% respectively compared with SINT on OTB 2013, which contains 51 video sequences. For the OTB 100, the average overlap ratio gain is 4.2%.en_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.rights
dc.subjectCNNen_US
dc.subjectModel updatingen_US
dc.subjectEnsemble Siamese Trackeren_US
dc.subjectSiamese instance search Trackeren_US
dc.titleSiamese network ensemble for visual trackingen_US
dc.typeArticleen_US
dc.date.updated2018-06-18T12:49:35Z
dc.language.isiEN-GB
dc.journal.titleNeurocomputing
dc.description.fulltextreserveden_US


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