5 accepted papers
In this paper, we present a new approach for efficient regression-based object tracking. Our approach is closely related to the Generic Object Tracking Using Regression Networks (GOTURN) framework [1]. We make the following contributions. First, we demonstrate that there is a theoretical relationshi…
Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - "on the fly" - how the object is changing over time. A fu…
In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking. The dataset consists of 100 videos (380K frames) captured with now commonly available higher frame rate (240 FPS) cameras from real world scenarios. All frames…
An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image. Hitherto, the problem has been addressed through the application of canonical deep learning methods to regress from the image directly to the 3D shape and pose labels. These approaches…
Correlation filters take advantage of specific properties in the Fourier domain allowing them to be estimated efficiently: O(ND log D) in the frequency domain, versus O(D^3 + ND^2) spatially where D is signal length, and N is the number of signals. Recent extensions to correlation filters, such as…