End-to-End Representation Learning for Correlation Filter Based Tracking

Jack Valmadre, Luca Bertinetto, Joao Henriques, Andrea Vedaldi, Philip H. S. Torr
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Training image: 255x255x3 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 search the "test image" by cross-correlation. Abstract The Correlation Filter is an algorithm that trains a linear
more » ... te to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates.
doi:10.1109/cvpr.2017.531 dblp:conf/cvpr/ValmadreBHVT17 fatcat:xrcp5d56xbbs5m5fohxy4opqlq