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Siamese Centerness Prediction Network for Real-Time Visual Object Tracking

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Abstract

Siamese network has been proven to achieve excellent results for visual object tracking where the SiamFC(Fully-Convolutional)is among the most well-known seminar work. Recently, with the successful application of the Region Proposal Network (RPN) in object detection, siamese networks combined with RPN have achieved good performance in visual tracking tasks. However, RPN requires the selection of the number, aspect ratio and size of the anchor boxes and these anchor-related parameters more often than not, need manual intervention and tuning. In this work, we first add a channel-aware module in the siamese network to obtain the more discriminative features. Thereafter, we propose an anchor-free strategy to replace the RPN module. The proposed framework consists of two networks, namely, the Siamese network and the Centerness Prediction network (CPN). We call the proposed method SiamCPN. In the Siamese network, Resnet50 is used as the backbone. SiamCPN is simple and relatively efficient due to the fact that it avoids the need for complicated hyper-parameters of the anchor boxes. Extensive experimental results on four visual tracking benchmark datasets, OTB100, VOT2016, UAV123 and LaSOT show that the proposed framework has achieved highly competitive and better performance compared with the state-of-the-art trackers. SiamCPN can run at 60 frames per second (FPS) on an AMD processor with 2 RTX3090.

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Funding

This paper is funded by National Key R &D Program of China (No.2021YFF0603904).

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Authors and Affiliations

  1. School of Intelligent Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Yue Wu & Chengtao Cai

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Chai Kiat Yeo

  3. Key laboratory of Intelligent Technology and Application of Marine Equipment (Harbin Engineering University), Ministry of Education Harbin, Heilongjiang Province, 150001, China

    Chengtao Cai

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  1. Yue Wu
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  2. Chengtao Cai
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  3. Chai Kiat Yeo
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Correspondence to Chengtao Cai.

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Wu, Y., Cai, C. & Yeo, C.K. Siamese Centerness Prediction Network for Real-Time Visual Object Tracking. Neural Process Lett 55, 1029–1044 (2023). https://doi.org/10.1007/s11063-022-10924-4

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