Robust Visual Tracking Based on Second Order Pooling Network

doi:10.3969/j.issn.0372-2112.2020.08.003

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ACTA ELECTRONICA SINICA ›› 2020, Vol. 48 ›› Issue (8) : 1472-1478. DOI: 10.3969/j.issn.0372-2112.2020.08.003

Robust Visual Tracking Based on Second Order Pooling Network

PU Lei¹, FENG Xin-xi², HOU Zhi-qiang³, YU Wang-sheng²

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Abstract

Aiming at the problem that the target is easy to lose in the complex scene such as low resolution, occlusion, the interference of similar objects, this paper proposes a visual tracking algorithm based on second-order pooling network. Most of the existing methods use the first-order pooling network, which makes the difference between similar targets insufficient. In this paper, based on the VGG16 network structure, the last first-order pooling layer is replaced by the second-order covariance pooling layer, and then the network is retrained on ImageNet and CUB200-2011 image data sets. In order to reduce the computational burden, only the fourth convolution feature of the pre-training network is extracted as the appearance representation of the target. Finally, the extracted features are combined with the existing correlation filtering algorithm. The experimental results show that the algorithm achieves excellent performance in tracking accuracy and success rate.

Key words

visual tracking / second-order pooling network / deep features / correlation filter

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PU Lei, FENG Xin-xi, HOU Zhi-qiang, YU Wang-sheng. Robust Visual Tracking Based on Second Order Pooling Network[J]. Acta Electronica Sinica, 2020, 48(8): 1472-1478. https://doi.org/10.3969/j.issn.0372-2112.2020.08.003

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