Temporal Up-Sampling for Asynchronous Events

TL;DR

This paper introduces a temporal up-sampling algorithm for event cameras that enhances event density and quality, thereby improving performance in tasks like image reconstruction and object detection, especially in low-brightness or slow-motion scenes.

Contribution

The paper presents a novel event temporal up-sampling method based on motion trajectory estimation and temporal point processes, addressing sparsity and noise issues in event data.

Findings

Improved image reconstruction quality.

Enhanced object detection accuracy.

More effective event information in challenging scenes.

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.