Abstract
The correlation filters tracker allows features from multiple channels. The fusion of features by simply summing over them in an Euclidean space would destroy the inherent geometry among multiple features, resulting in the lose of their phase information which is crucial to tracking task. To provide a better fusion of features from multiple layers of a convolutional neural network (CNN) in the classical CNNs based correlation filters algorithm, we introduce spherical manifolds and computing intrinsic mean on spherical manifolds in the article, so that fusion of features and online update of filter kernels can be implemented over a spherical manifolds. In addition, we introduce a random projection method, imposed on CNN features before feature fusion to compress features for the sake of reducing computational complexity and modeling complexity. Extensive experiments on OTB-50 dataset demonstrate that the proposed algorithm outperforms state-of-the-art methods with respect to both precision and success rate.
Highlights
The mainly task of tracking is estimating the location of a visual target in each frame of an image sequence
The proposed algorithm Dual Correlation Filter (DCF) is implemented in MATLAB 2016a and runs on the same CPU with the standard parameters provided by the authors
In this article, we propose a novel correlation filtering tracking algorithm based on spherical manifold geometry
Summary
The mainly task of tracking is estimating the location of a visual target in each frame of an image sequence. It has various practical applications, especially for human-machine interactions, visual surveillance and unmanned control systems [1]–[3]. Despite significant progress has been achieved in recent years, object tracking is still one of the most challenging problems in computer vision owing to factors, such as partial occlusion, deformation, scale variations, illumination variation, background clutter, in-plane/out-of-plane rotations and motion blur [4]–[6]. Correlation filter (CF) based discriminative algorithms have gained high attention owning to high accuracy of object tracking and low computational complexity [7].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
