Learning spatially correlation filters based on convolutional features via PSO algorithm and two combined color spaces for visual tracking

摘要

Last years, we have seen an emergence of wide methods in visual object tracking topic as convolutional neural network combined with correlation filter such as hierarchical features (HCF) (Ma et al. 20). However, upon the fact that some features may cause the tracking failures, the existing methods are still suffering of handling complex object appearance changes such as fast motion, significant deformation and occlusions. Further, they learn the correlation filter in frequency domain using Fourier transform, which cause unwanted boundary effects, which severely degrade the quality of the tracking model. Moreover, these methods are incapable of dealing with the illumination variation because they rely only on RGB base for color sequences. In this paper, we propose a novel method, which addresses the pre-cited problems. As first contribution, we learn adaptively three correlation filters in the spatial domain, with hierarchical convolutional features extracted from specific layers. Indeed, we apply the Particle Swarm Optimization algorithm to solve the update model equation of the correlation filters. Second, we propose that the switching between RGB and HSV color bases, give a soft manner to handle the illumination variation. For this aim, an HSV-energy condition is presented to choose the appropriate color base resorting to the energy of the second HSV component. Extensive experiments on a common benchmark dataset, justify that the proposed method outperforms the state-of-art methods.