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arXiv · 2208.08721

Temporal Up-Sampling for Asynchronous Events

Xijie Xiang·Lin Zhu·Jianing Li·Yonghong Tian·Tiejun Huang
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Abstract

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 up-sampling 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.

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Xijie Xiang, Lin Zhu, Jianing Li, Yonghong Tian, Tiejun Huang. 2022-08-18. Temporal Up-Sampling for Asynchronous Events. https://arxiv.org/abs/2208.08721

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