Towards Mobile Sensing with Event Cameras on High-agility Resource-constrained Devices: A Survey

• URL: http://arxiv.org/abs/2503.22943v2
• Date: Thu, 03 Apr 2025 21:53:09 GMT
• Title: Towards Mobile Sensing with Event Cameras on High-agility Resource-constrained Devices: A Survey
• Authors: Haoyang Wang, Ruishan Guo, Pengtao Ma, Ciyu Ruan, Xinyu Luo, Wenhua Ding, Tianyang Zhong, Jingao Xu, Yunhao Liu, Xinlei Chen,
• Abstract summary: This paper surveys the literature over the period 2014-2024.<n>It provides a comprehensive overview of event-based mobile sensing systems.<n>We discuss key applications of event cameras in mobile sensing, including visual odometry, object tracking, optical flow estimation, and 3D reconstruction.
• Score: 21.038748549750395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing complexity of mobile device applications, these devices are evolving toward high agility. This shift imposes new demands on mobile sensing, particularly in terms of achieving high accuracy and low latency. Event-based vision has emerged as a disruptive paradigm, offering high temporal resolution, low latency, and energy efficiency, making it well-suited for high-accuracy and low-latency sensing tasks on high-agility platforms. However, the presence of substantial noisy events, the lack of inherent semantic information, and the large data volume pose significant challenges for event-based data processing on resource-constrained mobile devices. This paper surveys the literature over the period 2014-2024, provides a comprehensive overview of event-based mobile sensing systems, covering fundamental principles, event abstraction methods, algorithmic advancements, hardware and software acceleration strategies. We also discuss key applications of event cameras in mobile sensing, including visual odometry, object tracking, optical flow estimation, and 3D reconstruction, while highlighting the challenges associated with event data processing, sensor fusion, and real-time deployment. Furthermore, we outline future research directions, such as improving event camera hardware with advanced optics, leveraging neuromorphic computing for efficient processing, and integrating bio-inspired algorithms to enhance perception. To support ongoing research, we provide an open-source \textit{Online Sheet} with curated resources and recent developments. We hope this survey serves as a valuable reference, facilitating the adoption of event-based vision across diverse applications.

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