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. 2021 Feb 21;21(4):1492. doi: 10.3390/s21041492

Table 7.

Convolutional neural network architectures for tracking.

Model Highlight Source Code (Framework) Reference
GOTURN [223] 100 fps, feed-forward network, object motion and appearance GitHub [224] (C++) [75]
SlowFast network [225] Low and high frame rates, slow and high pathways, lightweight network GitHub [226] (PyTorch) [47]
Two-stream CNN [221] Complementary information on appearance, motion between frames, GitHub [227] (Python) [80]
(Inception, ResNet, VGG, and Xception) with LSTM [222] Recurrent convolution, CNN, doubly deep in spatial and temporal layers, LSTM GitHub [228] (PyTorch)
GitHub [229] (PredNet)
[86,87,119], etc.

Note: CNN is convolutional neural network; GOTRUN is generic object tracking using regression networks; LSTM is long short-term memory; ResNet is residual network; and VGG is visual geometry group.