MambaEVT: Event Stream based Visual Object Tracking using State Space Model

Understand

Event camera-based visual tracking has drawn more and more attention in recent years due to the unique imaging principle and advantages of low energy consumption, high dynamic range, and dense temporal resolution.

• Current event-based tracking algorithms are gradually hitting their performance bottlenecks, due to the utilization of vision Transformer and the static template for target object localization.
• In this paper, we propose a novel Mamba-based visual tracking framework that adopts the state space model with linear complexity as a backbone network.
• The search regions and target template are fed into the vision Mamba network for simultaneous feature extraction and interaction.