  Recently spiking neural networks (SNNs), the third-generation of neural
networks has shown remarkable capabilities of energy-efficient computing, which
is a promising alternative for deep neural networks (DNNs) with high energy
consumption. SNNs have reached competitive results compared to DNNs in
relatively simple tasks and small datasets such as image classification and
MNIST/CIFAR, while few studies on more challenging vision tasks on complex
datasets. In this paper, we focus on extending deep SNNs to object tracking, a
more advanced vision task with embedded applications and energy-saving
requirements, and present a spike-based Siamese network called SiamSNN.
Specifically, we propose an optimized hybrid similarity estimation method to
exploit temporal information in the SNNs, and introduce a novel two-status
coding scheme to optimize the temporal distribution of output spike trains for
further improvements. SiamSNN is the first deep SNN tracker that achieves short
latency and low precision loss on the visual object tracking benchmarks
OTB2013/2015, VOT2016/2018, and GOT-10k. Moreover, SiamSNN achieves notably low
energy consumption and real-time on Neuromorphic chip TrueNorth.
