Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors

Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian d...
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Autor*in:	Guang Chen [verfasserIn] Hu Cao [verfasserIn] Canbo Ye [verfasserIn] Zhenyan Zhang [verfasserIn] Xingbo Liu [verfasserIn] Xuhui Mo [verfasserIn] Zhongnan Qu [verfasserIn] Jörg Conradt [verfasserIn] Florian Röhrbein [verfasserIn] Alois Knoll [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	neuromorphic vision sensor event-stream encoding object detection convolutional neural network multi-Cue event information fusion

Übergeordnetes Werk:	In: Frontiers in Neurorobotics - Frontiers Media S.A., 2008, 13(2019)
Übergeordnetes Werk:	volume:13 ; year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnbot.2019.00010

Katalog-ID:	DOAJ01319125X

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language	English
source	In Frontiers in Neurorobotics 13(2019) volume:13 year:2019
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topic_facet	neuromorphic vision sensor event-stream encoding object detection convolutional neural network multi-Cue event information fusion Neurosciences. Biological psychiatry. Neuropsychiatry
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container_title	Frontiers in Neurorobotics
authorswithroles_txt_mv	Guang Chen @@aut@@ Hu Cao @@aut@@ Canbo Ye @@aut@@ Zhenyan Zhang @@aut@@ Xingbo Liu @@aut@@ Xuhui Mo @@aut@@ Zhongnan Qu @@aut@@ Jörg Conradt @@aut@@ Florian Röhrbein @@aut@@ Alois Knoll @@aut@@
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callnumber-first	R - Medicine
author	Guang Chen
spellingShingle	Guang Chen misc RC321-571 misc neuromorphic vision sensor misc event-stream encoding misc object detection misc convolutional neural network misc multi-Cue event information fusion misc Neurosciences. Biological psychiatry. Neuropsychiatry Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors
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topic_title	RC321-571 Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors neuromorphic vision sensor event-stream encoding object detection convolutional neural network multi-Cue event information fusion
topic	misc RC321-571 misc neuromorphic vision sensor misc event-stream encoding misc object detection misc convolutional neural network misc multi-Cue event information fusion misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc neuromorphic vision sensor misc event-stream encoding misc object detection misc convolutional neural network misc multi-Cue event information fusion misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors
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title_full	Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors
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title_sort	multi-cue event information fusion for pedestrian detection with neuromorphic vision sensors
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title_auth	Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors
abstract	Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors.
abstractGer	Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors.
abstract_unstemmed	Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors.
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title_short	Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors
url	https://doi.org/10.3389/fnbot.2019.00010 https://doaj.org/article/3505b20327c14b64bc5426d94f743807 https://www.frontiersin.org/article/10.3389/fnbot.2019.00010/full https://doaj.org/toc/1662-5218
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up_date	2024-07-03T16:16:14.557Z
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Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors

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