  Spiking neural network (SNN) is a biologically-plausible model and exhibits
advantages of high computational capability and low power consumption. While
the training of deep SNN is still an open problem, which limits the real-world
applications of deep SNN. Here we propose a deep SNN architecture named Spiking
SiamFC++ for object tracking with end-to-end direct training. Specifically, the
AlexNet network is extended in the time domain to extract the feature, and the
surrogate gradient function is adopted to realize direct supervised training of
the deep SNN. To examine the performance of the Spiking SiamFC++, several
tracking benchmarks including OTB2013, OTB2015, VOT2015, VOT2016, and UAV123
are considered. It is found that, the precision loss is small compared with the
original SiamFC++. Compared with the existing SNN-based target tracker, e.g.,
the SiamSNN, the precision (succession) of the proposed Spiking SiamFC++
reaches 85.24% (64.37%), which is much higher than that of 52.78% (44.32%)
achieved by the SiamSNN. To our best knowledge, the performance of the Spiking
SiamFC++ outperforms the existing state-of-the-art approaches in SNN-based
object tracking, which provides a novel path for SNN application in the field
of target tracking. This work may further promote the development of SNN
algorithms and neuromorphic chips.
