7,358 research outputs found
A sparsity-driven approach to multi-camera tracking in visual sensor networks
In this paper, a sparsity-driven approach is presented for multi-camera tracking in visual sensor networks (VSNs). VSNs consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. Motivated by the goal of tracking in a bandwidth-constrained environment, we present a sparsity-driven method to compress the features extracted by the camera nodes, which are then transmitted across the network for distributed inference. We have designed special overcomplete dictionaries that match the structure of the features, leading to very parsimonious yet accurate representations. We have tested our method in indoor and outdoor people tracking scenarios. Our experimental results demonstrate how our approach leads to communication savings without significant loss in tracking performance
I'm sorry to say, but your understanding of image processing fundamentals is absolutely wrong
The ongoing discussion whether modern vision systems have to be viewed as
visually-enabled cognitive systems or cognitively-enabled vision systems is
groundless, because perceptual and cognitive faculties of vision are separate
components of human (and consequently, artificial) information processing
system modeling.Comment: To be published as chapter 5 in "Frontiers in Brain, Vision and AI",
I-TECH Publisher, Viena, 200
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