Temporal Up-Sampling for Asynchronous Events
This addresses a domain-specific challenge for event-based vision systems, offering an incremental improvement in handling sparse event data.
The paper tackles the problem of sparse and noisy events from event cameras in low-brightness or slow-moving scenes by proposing a temporal up-sampling algorithm to generate more effective events, which improves downstream tasks like image reconstruction and object detection.
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.