Tracking With CNN Based Correlation Filters on Spherical Manifolds

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.