Abstract
In most existing hierarchical convolution feature-based trackers, the extracted target features are redundant or insufficient to achieve accurate and robust tracking. To cope with this issue, we propose an adaptive target tracking based on channel attention and hierarchical convolutional features. First, we extract multi-layer features using VGG-M network to represent the different semantic information of the target. Channel attention module is introduced to obtain the weights of each channel for ensuring adaptation of our method to the target deformation. Then, we train the correlation filters of each layer online and compute the response map independently. To better overcome feature excessiveness, we fuse the corresponding responses by an adaptive fusion scheme. Finally, the exhaustive experimental analysis on public datasets OTB2015 and VOT2017 shows that the proposed algorithm outperforms several state-of-the-art algorithms and can track the target stably even in the case of disturbance.
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This work is supported by the National Key Research and Development Project of China (2018YFB1601200).
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Wang, H., Zhang, H. Adaptive target tracking based on channel attention and multi-hierarchical convolutional features. Pattern Anal Applic 25, 305–313 (2022). https://doi.org/10.1007/s10044-021-01043-2
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DOI: https://doi.org/10.1007/s10044-021-01043-2