A Multimodal RGB and Events Dataset for Hand Detection in First-Person View

• URL: http://arxiv.org/abs/2606.10790v1
• Date: Tue, 09 Jun 2026 12:40:47 GMT
• Title: A Multimodal RGB and Events Dataset for Hand Detection in First-Person View
• Abstract summary: Existing hand detection algorithms work on images and the detection rate is restricted by the frame rate of the camera.<n>Recent work has shown that using a stereo setup of an event-based and a frame-based camera improves detection accuracy and the bandwidth-latency tradeoff.<n>We propose a methodology and an exemplary synthetic event-based hand dataset from an egocentric, first-person view perspective.
• Score: 2.5043709580004787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing hand detection algorithms work on images and the detection rate is restricted by the frame rate of the camera. In hand detection applications for moving robotic systems, conventional cameras cause motion blur, especially in darker lighting conditions. We can leverage the use of event-based cameras which possess a high dynamic range, high temporal resolution, and low power consumption. Recent work has shown that using a stereo setup of an event-based and a frame-based camera improves detection accuracy and the bandwidth-latency tradeoff. The main bottleneck in using event-based cameras in object detection and recognition tasks is a relatively low amount of training data. In this work, we propose a methodology and an exemplary synthetic event-based hand dataset from an egocentric, first-person view perspective. The data is synthesized from the existing RGB Egohands dataset with the v2e toolbox. Parameters of the v2e toolbox are varied to provide versions of the dataset with different lighting conditions and scales. Ground truth detections are generated with a fine-tuned YOLOv8 model which is applied to the RGB images in the Egohands dataset and interpolated on the high-temporal resolution events. We use the multi-modal dataset to perform hand detection with existing object detection algorithms which use a multi-modal setup of event and RGB cameras and demonstrate performance comparable to the state-of-the-art.

Related papers

• SkyEV: RGB-Event UAV detection and tracking dataset and baseline [7.035143064175411]
  We introduce SkyEV, an open-source dataset featuring highly synchronized RGB and event-based data.<n>SkyEV distinguishes itself by capturing complex real-world conditions, including camera motion and varied optical setups.<n>We provide a unified data loader and establish an experimental baseline, demonstrating the dataset's efficacy in detecting challenging, small-scale targets.
  arXiv  Detail & Related papers  (2026-07-21T06:12:06Z)
• FEMOT: Multi-Object Tracking using Frame and Event Cameras [57.75021868843091]
  Bio-inspired event cameras offer high temporal resolution and high dynamic range, providing complementary cues under extreme scenarios.<n>FEMOT is a large-scale RGB-event multi-object tracking dataset that covers diverse real-world scenarios and 14 challenging attributes.<n>FEMOTR is a multimodal tracking framework that decouples RGB and event features and fuses them in the frequency domain.
  arXiv  Detail & Related papers  (2026-06-12T04:17:41Z)
• EventGait: Towards Robust Gait Recognition with Event Streams [50.890621860023]
  Event cameras offer microsecond temporal resolution and high dynamic range, naturally capturing robust dynamic cues and suppressing static noise.<n>We propose textbfEventGait, an end-to-end dual-stream framework that separately models motion and shape while preserving the advantages of events.<n>Our approach sets a new state of the art on both synthesized and real-world event-based gait benchmarks, highlighting the robustness and potential of event-driven gait analysis.
  arXiv  Detail & Related papers  (2026-05-21T08:12:04Z)
• Exploring Spatial-Temporal Dynamics in Event-based Facial Micro-Expression Analysis [0.0]
  We introduce a novel, preliminary multi-resolution and multi-modal micro-expression dataset recorded with synchronized RGB and event cameras.<n>We show that event-based data can be used for micro-expression recognition and frame reconstruction.
  arXiv  Detail & Related papers  (2025-08-16T09:03:20Z)
• EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset [55.12137324648253]
  Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed. This paper focuses on five event-aided image and video enhancement tasks.
  arXiv  Detail & Related papers  (2023-12-13T15:42:04Z)
• SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features [52.213656737672935]
  SpikeMOT is an event-based multi-object tracker. SpikeMOT uses spiking neural networks to extract sparsetemporal features from event streams associated with objects.
  arXiv  Detail & Related papers  (2023-09-29T05:13:43Z)
• Deformable Neural Radiance Fields using RGB and Event Cameras [65.40527279809474]
  We develop a novel method to model the deformable neural radiance fields using RGB and event cameras. The proposed method uses the asynchronous stream of events and sparse RGB frames. Experiments conducted on both realistically rendered graphics and real-world datasets demonstrate a significant benefit of the proposed method.
  arXiv  Detail & Related papers  (2023-09-15T14:19:36Z)
• Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation [79.02808071245634]
  Event-based cameras are bio-inspired sensors that capture brightness change of every pixel in an asynchronous manner. Event streams are divided into grids in the x-y-t coordinates for both positive and negative polarity, producing a set of pillars as 3D tensor representation. Long memory is encoded in the hidden state of adaptive convLSTMs while short memory is modeled by computing spatial-temporal correlation between event pillars.
  arXiv  Detail & Related papers  (2023-03-17T12:12:41Z)
• Moving Object Detection for Event-based vision using Graph Spectral Clustering [6.354824287948164]
  Moving object detection has been a central topic of discussion in computer vision for its wide range of applications. We present an unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data. We additionally show how the optimum number of moving objects can be automatically determined.
  arXiv  Detail & Related papers  (2021-09-30T10:19:22Z)
• Learning Monocular Dense Depth from Events [53.078665310545745]
  Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames. Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
  arXiv  Detail & Related papers  (2020-10-16T12:36:23Z)
• Learning to Detect Objects with a 1 Megapixel Event Camera [14.949946376335305]
  Event cameras encode visual information with high temporal precision, low data-rate, and high-dynamic range. Due to the novelty of the field, the performance of event-based systems on many vision tasks is still lower compared to conventional frame-based solutions.
  arXiv  Detail & Related papers  (2020-09-28T16:03:59Z)

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.