Bina-Rep Event Frames: a Simple and Effective Representation for Event-based cameras

• URL: http://arxiv.org/abs/2202.13662v1
• Date: Mon, 28 Feb 2022 10:23:09 GMT
• Title: Bina-Rep Event Frames: a Simple and Effective Representation for Event-based cameras
• Authors: Sami Barchid, Jos\'e Mennesson and Chaabane Dj\'eraba
• Abstract summary: "Bina-Rep" is a simple representation method that converts asynchronous streams of events from event cameras to a sequence of sparse and expressive event frames. Our method is able to obtain sparser and more expressive event frames thanks to the retained information about event orders in the original stream.
• Score: 1.6114012813668934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents "Bina-Rep", a simple representation method that converts asynchronous streams of events from event cameras to a sequence of sparse and expressive event frames. By representing multiple binary event images as a single frame of $N$-bit numbers, our method is able to obtain sparser and more expressive event frames thanks to the retained information about event orders in the original stream. Coupled with our proposed model based on a convolutional neural network, the reported results achieve state-of-the-art performance and repeatedly outperforms other common event representation methods. Our approach also shows competitive robustness against common image corruptions, compared to other representation techniques.

Related papers

• Text-to-Events: Synthetic Event Camera Streams from Conditional Text Input [8.365349007799296]
  Event cameras are advantageous for tasks that require vision sensors with low-latency and sparse output responses. This paper reports a method for creating new labelled event datasets by using a text-to-X model. We demonstrate that the model can generate realistic event sequences of human gestures prompted by different text statements.
  arXiv  Detail & Related papers  (2024-06-05T16:34:12Z)
• Event-aware Video Corpus Moment Retrieval [79.48249428428802]
  Video Corpus Moment Retrieval (VCMR) is a practical video retrieval task focused on identifying a specific moment within a vast corpus of untrimmed videos. Existing methods for VCMR typically rely on frame-aware video retrieval, calculating similarities between the query and video frames to rank videos. We propose EventFormer, a model that explicitly utilizes events within videos as fundamental units for video retrieval.
  arXiv  Detail & Related papers  (2024-02-21T06:55:20Z)
• Representation Learning on Event Stream via an Elastic Net-incorporated Tensor Network [1.9515859963221267]
  We present a novel representation method which can capture global correlations of all events in the event stream simultaneously. Our method can achieve effective results in applications like filtering noise compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-16T02:51:47Z)
• Neuromorphic Imaging and Classification with Graph Learning [11.882239213276392]
  Bio-inspired neuromorphic cameras asynchronously record pixel brightness changes and generate sparse event streams. Due to the multidimensional address-event structure, most existing vision algorithms cannot properly handle asynchronous event streams. We propose a new graph representation of the event data and couple it with a Graph Transformer to perform accurate neuromorphic classification.
  arXiv  Detail & Related papers  (2023-09-27T12:58:18Z)
• Graph-based Asynchronous Event Processing for Rapid Object Recognition [59.112755601918074]
  Event cameras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes. We introduce a novel graph-based framework for event cameras, namely SlideGCN. Our approach can efficiently process data event-by-event, unlock the low latency nature of events data while still maintaining the graph's structure internally.
  arXiv  Detail & Related papers  (2023-08-28T08:59:57Z)
• Event Transformer [43.193463048148374]
  Event camera's low power consumption and ability to capture microsecond brightness make it attractive for various computer vision tasks. Existing event representation methods typically convert events into frames, voxel grids, or spikes for deep neural networks (DNNs) This work introduces a novel token-based event representation, where each event is considered a fundamental processing unit termed an event-token.
  arXiv  Detail & Related papers  (2022-04-11T15:05:06Z)
• MEFNet: Multi-scale Event Fusion Network for Motion Deblurring [62.60878284671317]
  Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution. In this paper, we rethink the event-based image deblurring problem and unfold it into an end-to-end two-stage image restoration network.
  arXiv  Detail & Related papers  (2021-11-30T23:18:35Z)
• Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation [57.22705137545853]
  We propose a task transfer method that allows models to be trained directly with labeled images and unlabeled event data. We leverage the generative event model to split event features into content and motion features. Our approach unlocks the vast amount of existing image datasets for the training of event-based neural networks.
  arXiv  Detail & Related papers  (2021-09-06T17:31:37Z)
• Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation [53.850686395708905]
  Event-based cameras record an asynchronous stream of per-pixel brightness changes. In this paper, we focus on single-layer architectures for representation learning from event data. We show improvements of up to 9 % in the recognition accuracy compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-23T10:40:03Z)
• Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning [63.91369308085091]
  We propose a novel and simple model for event sequence generation and explore temporal relationships of the event sequence in the video. The proposed model omits inefficient two-stage proposal generation and directly generates event boundaries conditioned on bi-directional temporal dependency in one pass. The overall system achieves state-of-the-art performance on the dense-captioning events in video task with 9.894 METEOR score on the challenge testing set.
  arXiv  Detail & Related papers  (2020-06-14T13:21:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.