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, 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.
Code (1)
Tasks
object-detectionObject DetectionPoint ProcessesvalidSimilar Papers 제목 키워드 기반
Asynchrony Increases Efficiency: Time Encoding of Videos and Low-Rank Signals
In event-based sensing, many sensors independently and asynchronously emit events when there is a change in their input. Event-based sensing can present significant improvements in power efficiency when compared to tradi…
ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data
We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing…
Gesture RecognitionAsynchronous Collaborative Graph Representation for Frames and Events
Integrating frames and events has become a widely accepted solution for various tasks in challenging scenarios. However, most multimodal methods directly convert events into image-like formats synchronized with frame…
Depth EstimationDomain Adaptationobject-detectionObject DetectionCombining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction
Event cameras are novel vision sensors that report per-pixel brightness changes as a stream of asynchronous "events". They offer significant advantages compared to standard cameras due to their high temporal resolution, …
Depth EstimationDepth PredictionMonocular Depth EstimationObject DetectionAEGNN: Asynchronous Event-based Graph Neural Networks
The best performing learning algorithms devised for event cameras work by first converting events into dense representations that are then processed using standard CNNs. However, these steps discard both the sparsity and…