title: MambaEVT: Event Stream based Visual Object Tracking using State Space
    Model
summary: 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
paper_authors._id: 66c550cc3496dcc9c2769d7a
paper_authors.name: Xiao Wang
paper_authors._id: 66c550cc3496dcc9c2769d7b
paper_authors.name: Chao wang
paper_authors._id: 66c550cc3496dcc9c2769d7c
paper_authors.name: Shiao Wang
paper_authors._id: 66c550cc3496dcc9c2769d7d
paper_authors.name: Xixi Wang
paper_authors._id: 66c550cc3496dcc9c2769d7e
paper_authors.name: Zhicheng Zhao
paper_authors._id: 66c550cc3496dcc9c2769d7f
paper_authors.name: Lin Zhu
paper_authors._id: 66c550cc3496dcc9c2769d80
paper_authors.name: Bo Jiang
paper_title: MambaEVT: Event Stream based Visual Object Tracking using State Space
    Model
paper_summary: 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
paper_ai_keywords: state space model vision Mamba networks feature extraction interaction tracking head target localization dynamic template update strategy Memory Mamba network template memory module EventVOT VisEvent FE240hz