Object Tracking Using Multiple Neuromorphic Vision Sensors

Becanovic, Vlatko; Hosseiny, Ramin; Indiveri, Giacomo

doi:10.1007/978-3-540-32256-6_36

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…However, most of the systems that make use of neuromorphic sensing implement only a single type of sensor such as vision or auditory. Rajapakse and Acharya ( 1991 ) and Bečanović et al ( 2005 ) were among the first who targeted the development of platforms for interfacing multiple neuromorphic sensors. Chan et al ( 2012 ) implemented sensor fusion of Audio-Video (AV) neuromorphic sensors and presented an advanced version of the Koala robot that was first developed by Bečanović et al ( 2005 ) for object tracking.…”

Section: Trends In Sensor Fusion Applicationmentioning

confidence: 99%

“…Rajapakse and Acharya ( 1991 ) and Bečanović et al ( 2005 ) were among the first who targeted the development of platforms for interfacing multiple neuromorphic sensors. Chan et al ( 2012 ) implemented sensor fusion of Audio-Video (AV) neuromorphic sensors and presented an advanced version of the Koala robot that was first developed by Bečanović et al ( 2005 ) for object tracking. Development of neuromorphic processing boards under large scale projects such as CAVIAR, BrainScaleS and SpiNNaker, promotes the idea of sensor fusion, and data correlation.…”

Section: Trends In Sensor Fusion Applicationmentioning

confidence: 99%

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A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors

2016

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Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI (analog Very Large Scale Integration) and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data. The foundation for research in neuromorphic sensors was laid more than two decades ago, but recent developments in understanding of biological sensing and advanced electronics, have stimulated research on sophisticated neuromorphic sensors that provide numerous advantages over conventional sensors. In this paper, we review the current state-of-the-art in neuromorphic implementation of vision, auditory, and olfactory sensors and identify key contributions across these fields. Bringing together these key contributions we suggest a future research direction for further development of the neuromorphic sensing field.

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Section: Trends In Sensor Fusion Applicationmentioning

confidence: 99%

Section: Trends In Sensor Fusion Applicationmentioning

confidence: 99%

A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors

2016

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“…One dimensional tracking chips have been used in a wide variety of applications, especially in the field of mobile robotics [33, 40–42]. In this domain typical applications require compact and power-efficient computing devices which should be robust to noise, tolerant to adverse conditions induced by the motion of the system ( e.g .…”

Section: A One-dimensional Tracker Chipmentioning

confidence: 99%

Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems

Indiveri

2008

Sensors

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Biological organisms perform complex selective attention operations continuously and effortlessly. These operations allow them to quickly determine the motor actions to take in response to combinations of external stimuli and internal states, and to pay attention to subsets of sensory inputs suppressing non salient ones. Selective attention strategies are extremely effective in both natural and artificial systems which have to cope with large amounts of input data and have limited computational resources. One of the main computational primitives used to perform these selection operations is the Winner-Take-All (WTA) network. These types of networks are formed by arrays of coupled computational nodes that selectively amplify the strongest input signals, and suppress the weaker ones. Neuromorphic circuits are an optimal medium for constructing WTA networks and for implementing efficient hardware models of selective attention systems. In this paper we present an overview of selective attention systems based on neuromorphic WTA circuits ranging from single-chip vision sensors for selecting and tracking the position of salient features, to multi-chip systems implement saliency-map based models of selective attention.

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“…Systems that combine multiple sensors exist but the sensors are still restricted to one modality, for example, vision (Becanovic et al, 2004). While audio–visual (AV) sensor fusion has been studied for a long time in the field of robotics, with examples such as (Bothe et al, 1999; Wong et al, 2008), to our knowledge, there are no neuromorphic systems which combine sensors of different modalities.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic audio–visual sensor fusion on a sound-localizing robot

Chan¹

2012

Front. Neurosci.

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This paper presents the first robotic system featuring audio–visual (AV) sensor fusion with neuromorphic sensors. We combine a pair of silicon cochleae and a silicon retina on a robotic platform to allow the robot to learn sound localization through self motion and visual feedback, using an adaptive ITD-based sound localization algorithm. After training, the robot can localize sound sources (white or pink noise) in a reverberant environment with an RMS error of 4–5° in azimuth. We also investigate the AV source binding problem and an experiment is conducted to test the effectiveness of matching an audio event with a corresponding visual event based on their onset time. Despite the simplicity of this method and a large number of false visual events in the background, a correct match can be made 75% of the time during the experiment.

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Object Tracking Using Multiple Neuromorphic Vision Sensors

Cited by 5 publications

References 6 publications

A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors

A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors

Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems

Neuromorphic audio–visual sensor fusion on a sound-localizing robot

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