Abstract
Spiking Neural Networks (SNNs) promise energy-efficient vision, but applying them to RGB visual tracking remains difficult: Existing SNN tracking frameworks either do not fully align with spike-driven computation or do not fully leverage neurons' spatiotemporal dynamics, leading to a trade-off between efficiency and accuracy. To address this, we introduce SpikeTrack, a spike-driven framework for energy-efficient RGB object tracking. SpikeTrack employs a novel asymmetric design that uses asymmetric timestep expansion and unidirectional information flow, harnessing spatiotemporal dynamics while cutting computation. To ensure effective unidirectional information transfer between branches, we design a memory-retrieval module inspired by neural inference mechanisms. This module recurrently queries a compact memory initialized by the template to retrieve target cues and sharpen target perception over time. Extensive experiments demonstrate that SpikeTrack achieves the state-of-the-art among SNN-based trackers and remains competitive with advanced ANN trackers. Notably, it surpasses TransT on LaSOT dataset while consuming only 1/26 of its energy. To our knowledge, SpikeTrack is the first spike-driven framework to make RGB tracking both accurate and energy efficient. The code and models are available at this https URL.
Abstract (translated)
脉冲神经网络（SNNs）承诺了节能的视觉处理，但将其应用于RGB视频目标跟踪仍然面临挑战：现有的基于SNN的目标追踪框架要么没有完全符合脉冲驱动计算的要求，要么没有充分利用神经元的空间-时间动态特性，导致效率与准确性之间的权衡。为了解决这个问题，我们提出了SpikeTrack，这是一个用于节能的RGB对象跟踪的脉冲驱动框架。 SpikeTrack采用了新颖的不对称设计，利用了不对称的时间步扩展和单向信息流，这样既发挥了空间-时间动态特性又能减少计算量。为了确保分支间有效的一方向信息传递，我们设计了一个受神经推理机制启发的记忆检索模块。此模块通过反复查询模板初始化的小型记忆来获取目标线索，并随着时间推移增强对目标的感知。 广泛的实验表明，SpikeTrack在基于SNN的目标追踪器中达到了最先进的性能，并且与高级人工神经网络（ANN）跟踪器保持了竞争力。尤其值得注意的是，在LaSOT数据集上，它超过了TransT，同时仅消耗后者1/26的能量。 据我们所知，SpikeTrack是第一个能够实现RGB跟踪既准确又节能的脉冲驱动框架。该代码和模型可在此链接获取（在原文中给出的具体链接需要用户自己访问查看）。
URL
https://arxiv.org/abs/2602.23963
