SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking

• URL: http://arxiv.org/abs/2602.23963v1
• Date: Fri, 27 Feb 2026 12:16:51 GMT
• Title: SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking
• Authors: Qiuyang Zhang, Jiujun Cheng, Qichao Mao, Cong Liu, Yu Fang, Yuhong Li, Mengying Ge, Shangce Gao,
• Abstract summary: We introduce SpikeTrack, a spike-driven framework for energy-efficient object tracking.<n>SpikeTrack employs a novel asymmetric design that uses asymmetric timestep expansion and unidirectional information flow.<n>SpikeTrack is the first spike-driven framework to make RGB tracking both accurate and energy efficient.
• Score: 21.128378159261008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/faicaiwawa/SpikeTrack.

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