DHP19: Dynamic Vision Sensor 3D Human Pose Dataset

Calabrese, Enrico; Taverni, Gemma; Easthope, Chris; Skriabine, Sophie; Corradi, Federico; Longinotti, Luca; Eng, Kynan; Delbrück, Tobi

doi:10.1109/cvprw.2019.00217

Cited by 76 publications

(75 citation statements)

References 29 publications

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“…This task has been targeted by the DVS-Gesture dataset [2] which contains 11 different gestures recorded by the DVS128 event camera. The first and so far only neuromorphic human pose dataset, DAVIS Human Pose Dataset(DHP19), has been recently introduced by [7]. It features four event cameras with resolution of 260 × 346 recording 33 different movements simultaneously from different viewpoints.…”

Section: Event Camera Datasets For Machine Learningmentioning

confidence: 99%

Video to Events: Recycling Video Datasets for Event Cameras

Gehrig

Hidalgo-Carrió

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

171

119

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Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high dynamic range (HDR), high temporal resolution, and no motion blur. Recently, novel learning approaches operating on event data have achieved impressive results. Yet, these methods require a large amount of event data for training, which is hardly available due the novelty of event sensors in computer vision research. In this paper, we present a method that addresses these needs by converting any existing video dataset recorded with conventional cameras to synthetic event data. This unlocks the use of a virtually unlimited number of existing video datasets for training networks designed for real event data. We evaluate our method on two relevant vision tasks, i.e., object recognition and semantic segmentation, and show that models trained on synthetic events have several benefits: (i) they generalize well to real event data, even in scenarios where standard-camera images are blurry or overexposed, by inheriting the outstanding properties of event cameras; (ii) they can be used for finetuning on real data to improve over state-of-the-art for both classification and semantic segmentation.

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Section: Event Camera Datasets For Machine Learningmentioning

confidence: 99%

Video to Events: Recycling Video Datasets for Event Cameras

Gehrig

Hidalgo-Carrió

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

171

119

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“…These type of optical sensors are often used in robotics [6,56] or classification tasks. Datasets for classification applications containing event camera-based recordings of MNIST, Caltech101, poker cards, or human postures are readily available [12,63,73,84].…”

Section: Temporal Contrast Codingmentioning

confidence: 99%

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

Auge

Hille

Mueller

et al. 2021

Neural Process Lett

126

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Biologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

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“…Benosman et al released the first real-world event-based dataset for object classification, namely, the N-CARS dataset which embodied cars at different poses or speeds and various background scenarios. e Dynamic Vision Sensor Human Pose dataset (DHP19) [87] served as a benchmark for human body movements and included a set of 33 movements with 17 subjects from a DAVIS 346 camera.…”

Section: Datasets and Simulatorsmentioning

confidence: 99%

Data‐Driven Technology in Event‐Based Vision

Sun

Shi²,

Zhang³

et al. 2021

Complexity

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Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware and datasets constitute an emerging field named event-based data-driven technology. Focusing on data-driven technology in event-based vision, this paper first explicates the operating principle, advantages, and intrinsic nature of event cameras, as well as background knowledge in event-based vision, presenting an overview of this research field. Then, we explain why event-based data-driven technology becomes a research focus, including reasons for the rise of event-based vision and the superiority of data-driven approaches over other event-based algorithms. Current status and future trends of event-based data-driven technology are presented successively in terms of hardware, datasets, and algorithms, providing guidance for future research. Generally, this paper reveals the great prospects of event-based data-driven technology and presents a comprehensive overview of this field, aiming at a more efficient and bio-inspired visual system to extract visual features from the external environment.

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DHP19: Dynamic Vision Sensor 3D Human Pose Dataset

Cited by 76 publications

References 29 publications

Video to Events: Recycling Video Datasets for Event Cameras

Video to Events: Recycling Video Datasets for Event Cameras

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

Data‐Driven Technology in Event‐Based Vision

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