Fully-Convolutional Siamese Networks for Object Tracking Fully-Convolutional Siamese Networks for Object Tracking
Paper summary Summary: This paper suggests an approach to find correlation score between different sub-window of a search image with a query image. Using a fully convolutional siamese network architecture that they describe helps in getting this correlation for different sub windows for search images in one forward pass of the network. For every video, they compute the features for the object being tracked once and use it for entire duration of video for computing correlation. My take: This is in the same spirit as GOTURN tracker. Although having fully convolutional helps in having translation invariance, it is not directly an advantage over predicting bounding boxes directly as adopted in GOTURN paper. Also, results are not directly comparable as this has been trained on a different data-set.
Fully-Convolutional Siamese Networks for Object Tracking
Luca Bertinetto and Jack Valmadre and João F. Henriques and Andrea Vedaldi and Philip H. S. Torr
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.CV


Your comment:

ShortScience.org allows researchers to publish paper summaries that are voted on and ranked!