发表机构
College of Computer Science and Engineering, Guilin University of Technology; School of Information Engineering, Wuhan University of Technology（桂林理工大学计算机科学与工程学院; 武汉理工大学信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SBMVTrack提出全脉冲框架，通过能量加权脉冲预算和掩码多视图目标建模，在降低能耗的同时保持无人机跟踪精度，实现更优的精度-能量权衡。
AI 中文摘要
凭借稀疏和事件驱动的计算，脉冲神经网络在实现精确且节能的无人机视觉跟踪方面展现出巨大潜力。然而，现有的基于SNN的跟踪器通常仅使用脉冲发放率进行能量评估，缺乏对实际脉冲活动的显式优化。为解决这一问题，我们提出了SBMVTrack，一个用于节能无人机跟踪的全脉冲框架。SBMVTrack引入了能量加权脉冲预算（EWSB）。EWSB根据每个脉冲层的计算成本对实际脉冲活动进行加权，并约束能量加权发放率和饱和活动，从而减少冗余的脉冲计算。为了在脉冲预算约束下提升跟踪性能，我们提出了掩码多视图目标建模（MVTM）。该方法将来自同一序列的初始模板、在线模板和搜索区域视为相关的时间视图，通过跨视图特征补全和身份一致性学习增强目标表示的鲁棒性。在多个基准上的大量实验表明，SBMVTrack有效降低了平均脉冲发放率和理论能耗，同时保持了有竞争力的跟踪性能，实现了更好的精度-能量权衡。源代码将在论文被接收后发布。
英文摘要
With sparse and event-driven computation, spiking neural networks show great potential for achieving accurate and power-efficient UAV visual tracking. However, existing SNN-based trackers typically use spike firing rates only for power consumption and lack explicit optimization of actual spike activity. Moreover, regulating spike activity alone does not explicitly encourage stable target representations under partial observations and temporal appearance changes. We propose SBMVTrack, a fully spiking tracking framework that combines spike activity regulation with complementary multi-view representation learning. Specifically, SBMVTrack introduces Energy-Weighted Spike Budgeting (EWSB), which incorporates layer-wise computational costs when regulating spike firing rates and penalizing saturated activations, thereby reducing redundant spike computation. To further improve target representations under the spike budget constraint, we introduce Masked Multi-View Target Modeling (MVTM), which treats the initial template, online template, and search region as temporal views of the same target. By aligning target embeddings between masked and corresponding unmasked views and enforcing cross-view identity consistency, MVTM encourages robustness to missing local cues and temporal appearance changes. Experiments on four UAV benchmarks demonstrate competitive tracking performance with a 24.1% reduction in estimated power consumption relative to the baseline. On VisDrone2018, SBMVTrack achieves a success rate of 70.0%, exceeding SpikeTrack by 9.7 percentage points while reducing estimated power consumption by 45.7%. The source code will be released upon acceptance.
