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

TL;DR

MambaEVT introduces a novel event-based visual tracking framework utilizing a state space model and dynamic template updates, achieving improved accuracy and efficiency on large-scale datasets.

Contribution

The paper proposes a Mamba-based tracking framework with a state space model and dynamic template update strategy, addressing performance bottlenecks of existing event-based trackers.

Findings

Achieves a good balance between accuracy and computational cost.

Performs well on multiple large-scale datasets.

Introduces a dynamic template update mechanism.

Abstract

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. The output tokens of search regions will be fed into the tracking head for target localization. More importantly, we consider introducing a dynamic template update strategy into the tracking framework using the Memory Mamba network. By considering the diversity of samples in the target template library and making appropriate adjustments to the template memory module, a more effective dynamic template can be integrated. The effective combination of dynamic and static templates allows our Mamba-based tracking algorithm to achieve a good balance between accuracy and computational cost on multiple large-scale datasets, including EventVOT, VisEvent, and FE240hz. The source code will be released on https://github.com/Event-AHU/MambaEVT