Real time motion estimation using a neural architecture implemented on GPUs

“…Further details of the image processing aspects of the surveillance system can be found in our previous work [10]. Furthermore, in [35], a comparison with other tracking and motion estimation methods can be found that demonstrates the accuracy of the system with a single camera.…”

Section: Gng-seqmentioning

confidence: 91%

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

Orts-Escolano

García-Rodríguez

Morell

et al. 2014

JSAN

Self Cite

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In this work, we present a multi-camera surveillance system based on the use of self-organizing neural networks to represent events on video. The system processes several tasks in parallel using GPUs (graphic processor units). It addresses multiple vision tasks at various levels, such as segmentation, representation or characterization, analysis and monitoring of the movement. These features allow the construction of a robust representation of the environment and interpret the behavior of mobile agents in the scene. It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from multiple acquisition devices at video frequency. Offering relevant information to higher level systems, monitoring and making decisions in real time, it must accomplish a set of requirements, such as: time constraints, high availability, robustness, high processing speed and re-configurability. We have built a system able to represent and analyze the motion in video acquired by a multi-camera network and to process multi-source data in parallel on a multi-GPU architecture.

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“…This application does not necessarily require a temporal component, as it can analyze video frames as images. However, some of the other applications in video do require a temporal component, including motion detection [383], [572], [1768], [1769], [2632], motion estimation [1031], [1704], [2633], motion tracking [905], [917], [2634], [2635], and activity recognition [916], [2636].…”

Section: Applicationsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

175

180

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Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

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Real time motion estimation using a neural architecture implemented on GPUs

Cited by 11 publications

References 57 publications

Fast Video Artistic Transfer via Motion Compensation

Fast Video Artistic Transfer via Motion Compensation

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

A Survey of Neuromorphic Computing and Neural Networks in Hardware

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