20160930-33 Luca Bertinetto：Fully-Convolutional Siamese Networks for...

报告嘉宾1: Luca Bertinetto (University of Oxford)

报告时间：2016年9月30日（星期五）晚20:00（北京时间）

主持人：郑帅

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 exploit the expressive power of deep convolutional networks. However, when the object to track is not known beforehand, it is necessary to perform Stochastic Gradient Descent online to adapt the weights of the network, severely compromising the speed of the system. In this paper we equip a basic tracking algorithm with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video. Our tracker operates at frame-rates beyond real-time and, despite its extreme simplicity, achieves state-of-the-art performance in multiple benchmarks。

Luca Bertinetto is a PhD candidate in the Torr Vision Group at the University of Oxford.

The main focus of his doctorate is the problem of agnostic object tracking, which he likes to tackle using simple and effective approaches.

Before getting lost among the spires of Oxford, he obtained a joint MSc in Computer Engineering between the Polytechnic University of Turin and Telecom Paris Tech. He has published at CVPR and NIPS and reviewed for PAMI.