Summary of Mambaevt: Event Stream Based Visual Object Tracking Using State Space Model, by Xiao Wang et al.
MambaEVT: Event Stream based Visual Object Tracking using State Space Model
by Xiao Wang, Chao wang, Shiao Wang, Xixi Wang, Zhicheng Zhao, Lin Zhu, Bo Jiang
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel visual tracking framework that leverages event cameras’ unique features and overcomes performance bottlenecks in current algorithms. The Mamba-based framework combines state space models with linear complexity as the backbone network, allowing for efficient feature extraction and interaction between search regions and target templates. Additionally, a dynamic template update strategy is introduced using Memory Mamba networks, enabling more effective tracking by considering sample diversity. Experimental results demonstrate the algorithm’s ability to achieve a good balance between accuracy and computational cost on multiple large-scale datasets, including EventVOT, VisEvent, and FE240hz. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new approach for visual tracking using event cameras. The Mamba-based framework is designed to overcome performance limitations in current algorithms by combining state space models with linear complexity and dynamic template updates. This allows the algorithm to efficiently process visual data while improving accuracy. The researchers test their method on multiple datasets and show it can achieve a good balance between accuracy and computational cost. |
Keywords
» Artificial intelligence » Feature extraction » Tracking
