Temporal Up-Sampling for Asynchronous Events

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,...

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Bibliographic Details
Main Authors: Xiang, Xijie, Zhu, Lin, Li, Jianing, Tian, Yonghong, Huang, Tiejun
Format: Conference Proceeding
Language:English
Subjects:
Brightness
Cameras
contrast maximization
event camera
Image resolution
Object detection
Reliability
temporal point process
Temporal up-sampling
Trajectory
Vision sensors
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Summary: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 algorithm 1 1 Code: https://github.com/XIJIE-XIANG/Event-Temporal-Up-sampling 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.
ISSN:1945-788X
DOI:10.1109/ICME52920.2022.9858934