Abstract
Object tracking has recently raised a great research interest, and many Siamese network-based trackers have achieved the state-of-the-art performances. However, by analyzing their network structure, it can be found that those feature extraction modules still use AlexNet or its variants. Compared with some recent lightweight networks, there is still much room for improvement in accuracy. In this paper, we proposed an improved lightweight Siamese network, which combine the MobileNetV2 and SiamRPN. By utilizing strategy of the improved network which expanding the feature channels at first then compressing it, more feature information of object can be obtained for object tracking and the accuracy of the tracking algorithm can be effectively improved. We demonstrate the tracking performance of the proposed algorithm on OTB-100 dataset, and the proposed algorithm has a better performance in AUC and accuracy than some state-of-the-art trackers, with a 0.646 AUC and a 0.877 precision.
The first author is a graduate student of Yunnan University.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China (Grant No. 61802337 and 11663007), China Postdoctoral Science Foundation funded project (Grant No. 2017M623323XB); Application Foundation Project of Yunnan Province of China (Grant No. 2016FB103)
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Liu, M., Lei, Q., Yu, L., Gao, Y., Zhang, X. (2019). Visual Object Tracking via an Improved Lightweight Siamese Network. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_25
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