TEMPORAL UP-SAMPLING for ASYNCHRONOUS EVENTS

作     者：Xiang, Xijie Zhu, Lin Li, Jianing Tian, Yonghong Huang, Tiejun 

作者机构：Department of Electrical and Computer Engineering Shanghai Jiao Tong University China School of Computer Science Peking University China Peng Cheng National Laboratory China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Luminance 

摘      要：The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid events directly affects the performance of event-based tasks, such as reconstruction, detection, and recognition. However, when in low-brightness or slow-moving scenes, events are often sparse and accompanied by noise, which poses challenges for event-based tasks. To solve these challenges, we propose an event temporal up-sampling algorithm1 to generate more effective and reliable events. The main idea of our algorithm is to generate up-sampling events on the event motion trajectory. First, we estimate the event motion trajectory by contrast maximization algorithm and then up-sampling the events by temporal point processes. Experimental results show that upsampling events can provide more effective information and improve the performance of downstream tasks, such as improving the quality of reconstructed images and increasing the accuracy of object detection. Copyright © 2022, The Authors. All rights reserved.