Robust online visual tracking with a single convolutional neural network
Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they require very long training time and a large number of training samples. In this work, we present an efficient and very robust online tracking algorithm using a single Convolutional Neural Network (CNN) for learning effective feature representations of the target object over time. Our contributions are multifold:...[Show more]
|Collections||ANU Research Publications|
|Source:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|01_Li_Robust_online_visual_tracking_2015.pdf||2.76 MB||Adobe PDF||Request a copy|
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