Luca Bertinetto

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Ming-Hsuan Yang24
João F. Henriques16
Philip H. S. Torr15
Andrew Zisserman15
Horst Possegger14
16Daniel Gordon
16Dieter Fox
16Ali Farhadi
16Fatih Murat Porikli
12A. Aydin Alatan

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The Visual Object Tracking VOT2015 Challenge Results
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on shortterm tracking to date. For each participating tracker, a short description… (More)
Fully-Convolutional Siamese Networks for Object Tracking
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)
The Visual Object Tracking VOT2016 Challenge Results
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art… (More)
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour… (More)
Learning feed-forward one-shot learners
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)
End-to-end representation learning for Correlation Filter based tracking
Test image: 255x255x3 17x17x32 49x49x32 Correlation Filter Crop ★ 33x33x1 CNN CNN 49x49x32 Figure 1: Overview of the proposed network architecture, CFNet. It is an asymmetric Siamese network: after applying the same convolutional feature transform to both input images, the " training image " is used to learn a linear template, which is then applied to… (More)
The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
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)
Shot-based object retrieval from video with compressed Fisher Vectors
This paper addresses the problem of retrieving those shots from a database of video sequences that match a query image. Existing architectures match the images using a high-level representation of local features extracted from the video database, and are mainly based on Bag ofWords model. Such architectures lack however the capability to scale up to very… (More)
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