@inproceedings{c6fb5abe7d584f5fa26be6d61022f961,
title = "Object tracking using deep convolutional neural networks and visual appearance models",
abstract = "In this paper we introduce a novel single object tracking method that extends the traditional GOTURN algorithm with a visual attention model. The proposed approach returns accurate object tracks and is able to handle sudden camera and background movement, long-term occlusions and multiple moving objects that can evolve simultaneously in a same neighborhood. The process of occlusion identification is performed using image quad-tree decomposition and patch matching, based on a convolution neural network trained offline. The object appearance model is adaptively modified in time based on both visual similarity constraints and trajectory verification tests. The experimental evaluation performed on the VOT 2016 dataset demonstrates the efficiency of our method that returns high accuracy scores regardless of the scene dynamics or object shape.",
keywords = "Adaptive object appearance model, Convolution neural networks, Occlusion detection, Patch matching, Single object tracking",
author = "Bogdan Mocanu and Ruxandra Tapu and Titus Zaharia",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 18th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2017 ; Conference date: 18-09-2017 Through 21-09-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-70353-4\_10",
language = "English",
isbn = "9783319703527",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "114--125",
editor = "Jacques Blanc-Talon and Dan Popescu and Paul Scheunders and Wilfried Philips and Rudi Penne",
booktitle = "Advanced Concepts for Intelligent Vision Systems - 18th International Conference, ACIVS 2017, Proceedings",
}