SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking

2026-02-27

AI Summary

This work addresses the challenge of achieving both high accuracy and energy efficiency in spiking neural network (SNN)-based visual tracking, a task further complicated by underutilization of spike-driven computation and neuronal spatiotemporal dynamics. To this end, we propose SpikeTrack—the first efficient SNN framework tailored for RGB object tracking—featuring asymmetric temporal step expansion, unidirectional information flow, and a memory retrieval module inspired by neural inference mechanisms. These components synergistically integrate spatiotemporal dynamics while significantly reducing computational overhead. Experimental results demonstrate that SpikeTrack surpasses state-of-the-art artificial neural network (ANN) trackers such as TransT on the LaSOT benchmark, establishing a new performance frontier in SNN-based tracking while consuming only 1/26th of their energy.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Web Mining and Content Analysis: Topic discovery and tracking

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.

Problem

• Spiking Neural Networks
• Visual Tracking
• Energy Efficiency
• RGB Object Tracking
• Spatiotemporal Dynamics

Innovation

• Spiking Neural Networks
• Visual Tracking
• Spatiotemporal Dynamics
• Energy Efficiency
• Memory Retrieval