Year: 2025
Type: article
Abstract: Event cameras offer significant advantages, including a wide dynamic range, high temporal resolution, and immunity to motion blur, making them highly promising for addressing challenging visual conditions. Extracting and utilizing effective information from asynchronous event streams is essential for the onboard implementation of event cameras. In this article, we propose a stream-lined event-based intensity reconstruction scheme, event-based single integration (ESI), to address such implementation challenges. This method guarantees the portability of conventional frame-based vision methods to event-based scenarios and maintains the intrinsic advantages of event cameras. The ESI approach reconstructs intensity images by performing a single integration of the event streams combined with an enhanced decay algorithm. Such a method enables real-time intensity reconstruction at a high frame rate [typically 100 frames per second (FPS)]. Furthermore, the relatively low computational load of ESI fits onboard implementation suitably, such as in uncrewed aerial vehicle (UAV)-based visual tracking scenarios. Extensive experiments have been conducted to evaluate the performance comparison of ESI and state-of-the-art algorithms. Compared with state-of-the-art algorithms, ESI demonstrates remarkable runtime efficiency improvements, superior reconstruction quality, and a high frame rate. As a result, ESI enhances the UAV onboard perception significantly under visual adversary surroundings. In-flight tests, ESI demonstrates the effective performance for UAV onboard visual tracking under extremely low illumination conditions (2–10 lx), whereas other comparative algorithms fail due to the insufficient frame rate, poor image quality, or limited real-time performance.
Source: IEEE Sensors Journal
Authors: Xin Dong, Yiwei Zhang, Yangjie Cui, Jinwu Xiang, Daochun Li, Zhan Tu
Institutions: Beihang University, Tianmushan Laboratory
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7081924241