Luca Bertinetto

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#1 DSST Staple DAT #20 #50 #90 #100 #1 colour histogram scores HOG #10 #50 Figure 1: Sometimes colour distributions are not enough to discriminate the target from the background. Conversely, template models (like HOG) depend on the spatial configuration of the object and perform poorly when this changes rapidly. Our tracker Staple can rely on the strengths(More)
The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself. Despite the success of these methods, their online-only approach inherently limits the richness of the model they can learn. Recently, several attempts have been made to(More)
One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-shot learning as they need large amounts of training data. In this paper, we propose a method to learn the parameters of a deep model in one(More)
The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual track-ers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 track-ers are presented. For(More)
Porto, the institutional repository of the Politecnico di Torino, is provided by the University Library and the IT-Services. The aim is to enable open access to all the world. Please share with us how this access benefits you. Your story matters. ABSTRACT This paper addresses the problem of retrieving those shots from a database of video sequences that(More)
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