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Spiking neural network (SNN) is a biologically-plausible model and exhibitsadvantages of high computational capability and low power consumption. Whilethe training of deep SNN is still an open problem, which limits the real-worldapplications of deep SNN. Here we propose a deep SNN architecture named SpikingSiamFC++ for object tracking with end-to-end direct training. Specifically, theAlexNet network is extended in the time domain to extract the feature, and thesurrogate gradient function is adopted to realize direct supervised training ofthe deep SNN. To examine the performance of the Spiking SiamFC++, severaltracking benchmarks including OTB2013, OTB2015, VOT2015, VOT2016, and UAV123are considered. It is found that, the precision loss is small compared with theoriginal 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 SpikingSiamFC++ outperforms the existing state-of-the-art approaches in SNN-basedobject tracking, which provides a novel path for SNN application in the fieldof target tracking. This work may further promote the development of SNNalgorithms and neuromorphic chips.

Spiking SiamFC++: Deep Spiking Neural Network for Object Tracking