Live demonstration: A dynamic vision sensor with direct logarithmic output and full-frame picture-on-demand

Guo, Menghan; Ding, Ruoxi; Chen, Shoushun

doi:10.1109/iscas.2016.7527274

Cited by 11 publications

(3 citation statements)

References 1 publication

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“…Delbruck 团队 [39] 后来在动态视觉传感器基础上增加独立的图像采样电路, 开发出双模的有源像素视觉传感器 (dynamic and active pixel vision sensor, DAVIS). 陈守顺团队 [40] 采用增加事件位宽的方法, 在事件中增加像素光强信息用来恢复图像信息, 开发出 CeleX 相机. 黄铁军团队 [41] 我们比较了全连接脉冲网络、脉冲卷积和本文所提轻量化脉冲识别网络的准确率和骨干网络参数量.…”

Section: 轻量化识别网络搭建unclassified

Towards event camera signal recognition using a lightweight spiking neural network

Liu¹,

Wu²,

Shi³

et al. 2023

Sci. Sin.-Inf.

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Event cameras are event-driven bio-inspired sensors, owing to the following advantages: high temporal resolution, high dynamic range, low power, and high imaging speed. Artificial neural networks (ANNs) cannot directly process their output spike signal. Spiking neural networks (SNNs) are neuromorphic computing methods with a high temporal resolution and are event-driven, which fits well with event cameras. However, deep SNNs require large storage space and neuronal computing resources, which limits their deployment in mobile edge computing. Based on the feature dimension mapping, this paper proposes a lightweight SNN for embedded devices to reduce storage requirements, improve efficiency, and enhance network performance. First, by analyzing the relationship between the parameter number and the fitting function of the network, the SNN parameter bottleneck is clear. Subsequently, a general lightweight feature extraction structure, SpikeFire, is proposed based on a lowdimensional feature extraction-fusion strategy, which significantly reduces network parameters on the premise that the basic properties of the receptive field and feature dimension are the same as those of a spiking convolution. Additionally, the module simulates the complex connection of the brain and adopts the skip connection, which not only increases the extraction of multi-scale information but also contributes to deep network optimization. Finally, the lightweight network is deployed in embedded hardware to build an event-driven imaging recognition system. Experiments show that our method can reduce the parameter number and improve the running speed without weakening the performance in public, self-built real scenes, and extreme imaging scene datasets.

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Section: 轻量化识别网络搭建unclassified

Towards event camera signal recognition using a lightweight spiking neural network

Liu¹,

Wu²,

Shi³

et al. 2023

Sci. Sin.-Inf.

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show abstract

“…Meanwhile, the AER protocol makes it capable of high-speed capturing and asynchronous transmission. Different from Dr. Delbruck's work, Prof. Chen's group from Nanyang Technological University develops another DVS camera named CeleX with higher resolution of 384x320 [11]. The spike generated from their camera not only carries the address and the timestamp, but also brings a nine-bit ADC (analog-to-digital converter) value.…”

Section: Dynamic Vision Sensormentioning

confidence: 99%

Spike Camera and Its Coding Methods

Dong

Huang

2017

2017 Data Compression Conference (DCC)

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This paper introduces a spike camera with a distinct video capture scheme and proposes two methods of decoding the spike stream for texture reconstruction. The spike camera captures light and accumulates the converted luminance intensity at each pixel. A spike is fired when the accumulated intensity exceeds the dispatch threshold. The spike stream generated by the camera indicates the luminance variation. Analyzing the patterns of the spike stream makes it possible to reconstruct the picture of any moment which enables the playback of high speed movement.

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“…e pixel structure is composed of three parts [10][11][12]. e first part is a current-voltage logarithmic conversion light sensor circuit for sensing light intensity.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Vision Sensor Tracking Method Based on Event Correlation Index

Feng

Zhang

et al. 2021

Complexity

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Dynamic vision sensor is a kind of bioinspired sensor. It has the characteristics of fast response, large dynamic range, and asynchronous output event stream. These characteristics make it have advantages that traditional image sensors do not have in the field of tracking. The output form of the dynamic vision sensor is asynchronous event stream, and the object information needs to be provided by the relevant event cluster. This article proposes a method based on the event correlation index to obtain the object’s position, contour, and other information and is compatible with traditional tracking methods. Experiments show that this method can obtain the position information of the moving object and its continuous motion trajectory and analyze the influence of the parameters on the tracking effect. This method will have broad application prospects in security, transportation, etc.

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Live demonstration: A dynamic vision sensor with direct logarithmic output and full-frame picture-on-demand

Cited by 11 publications

References 1 publication

Towards event camera signal recognition using a lightweight spiking neural network

Towards event camera signal recognition using a lightweight spiking neural network

Spike Camera and Its Coding Methods

Dynamic Vision Sensor Tracking Method Based on Event Correlation Index

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