| 引用本文: | 黄庆坤,陈云华,张灵,兰浩鑫.时域感兴趣区域精确定位与膜电位多核调整的 动态视觉传感器数据分类[J].控制理论与应用,2020,37(8):1837~1845.[点击复制] |
| HUANG Qing-kun,CHEN Yun-hua,ZHANG Ling,LAN hao-xin.Dynamic vision sensors data classification based on precise temporal region of interest locating and multi-kernel membrane potential adjusting[J].Control Theory and Technology,2020,37(8):1837~1845.[点击复制] |
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| 时域感兴趣区域精确定位与膜电位多核调整的 动态视觉传感器数据分类 |
| Dynamic vision sensors data classification based on precise temporal region of interest locating and multi-kernel membrane potential adjusting |
| 摘要点击 2102 全文点击 824 投稿时间：2019-12-11 修订日期：2020-03-08 |
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| DOI编号 10.7641/CTA.2020.91001 |
| 2020,37(8):1837-1845 |
| 中文关键词 动态视觉传感器DVS DVS数据分类 目标识别 时域感兴趣区域ROI 神经网络 MK–Tempotron |
| 英文关键词 dynamic vision sensors DVS data classification objects recognition temporal region of interest neural networks MK–Tempotron |
| 基金项目 广东省自然科学基金项目（2016A030313713）;广东省交通运输厅科技项目（科技-2016-02-030） |
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| 中文摘要 |
| 动态视觉传感器(DVS)因其在获取视觉信息时具有低功耗, 低延迟等特性, 本质上十分适用于便携式设备
上的实时动作识别. 在对DVS事件流时域感兴趣区域(ROI)进行定位与分割时, 现有方法往往不能根据不同物体运
动自适应地设定最佳检测阈值、无法对静态场景中少量背景噪声进行过滤, 为此, 提出基于LIF神经元模型和脉冲
最大值监测单元的运动符号检测(MSD), 以实现在多种不同物体运动下事件流时域ROI关键时间点的自适应精确
定位; 在对分类器进行训练时, 对不同的脉冲输入模式, 使用不同的核函数调整突触后神经元膜电位, 使训练得到的
突触权重朝着正确发放的方向改变, 提出一种具有抗噪性的脉冲神经网络学习算法MK–Tempotron. 实验结果表明,
与同类方法相比, 本文方法在DVS数据集上的识别精度能获得高达14.61%的提升. |
| 英文摘要 |
| Dynamic vision sensors (DVS), due to their low power consumption and low latency when acquiring visual
information, are essentially suitable for real-time motion recognition on portable devices. When locating and segmenting
the temporal region of interest (ROI) of the DVS event stream, the existing methods often cannot adaptively set the optimal
detection threshold according to the motion of different objects, and they cannot eliminate a small amount of background
noise in a static scene either. To solve these problems, a motion symbol detection (MSD) method based on the leaky
integrate-and-fire (LIF) neuron model and a peak spiking monitoring unit is proposed, to precisely locate the critical time
point for the temporal ROI in the event stream containing a variety of different moving objects. And an anti-noise learning
algorithm based on the tempotron rule, which is named as MK–Tempotron, is proposed to deal with background noise in
static scenes. In MK–Tempotron, different kernels are applied according to different input spiking patterns to adjust the
post-synaptic membrane potential of the neuron during training, so that the synaptic weights can be changed in the direction
of correct firing of spikes, thus the anti-noise performance of the classification algorithm is improved. Experimental results
show that compared with some similar methods, the recognition accuracy of the proposed method on several DVS datasets
can be improved by as much as 14.61%. |
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