Online Information Augmented SiamRPN

  • Edward Budiman Sutanto
  • Sukho LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


Recently, many Siamese network based object tracking methods have been proposed and have shown good performances. These method give two images to two identical artificial neural networks as the inputs and find the target area based on the similarity measured by the Siamese network. However, the measure used in the Siamese network is based on the offline training, and therefore, easily fail to adapt to online changes. In this paper, we propose to apply a distance measure which considers the relative position between the objects and the histogram information as additional online information. This additional information prevents the tracking to fail when hard negative cases appear in the scene.


Object tracking Siamese network Histogram Deep learning 



This work was supported by the Technology development Program (S2644388) funded by the Ministry of SMEs and Startups (MSS, Korea) and the Basic Science Research Program through the National Research Foundation of Korea (NRF-2019R1I1A3A01060150).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dongseo UniversityBusanKorea

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