We address the problem of detecting non-transient anomalies in visual information. By non-transient anomalies we mean changes in the way environments look that are persistent across time. Such changes may include leav...
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ISBN:
(纸本)9781457706783
We address the problem of detecting non-transient anomalies in visual information. By non-transient anomalies we mean changes in the way environments look that are persistent across time. Such changes may include leaving unattended bags at airport corridors, putting graffiti in building walls or damaging public property. Detecting non-transient anomalies is critical to security and surveillance in indoor and outdoor environments. We argue that existing off-the-shelf solutions to computer vision problems (e. g., image recognition, gesture recognition, text recognition) are not the most efficient when applied to detecting non-transient anomalies due to their associated computational overhead. In this paper we present a neural network-based architecture that addresses some of the limitations of the state of the art. To speed up computations, our architecture supports the processing of a large number of neurons in parallel. To reduce computational overheads, our architecture omits some of the Gaussian kernel-based feature extraction tasks performed by other systems. To classify visual anomalies as non-transient, our architecture uses a codebook-based algorithm which builds a history profile for every image segment. We describe our architecture and present some performance analysis.
Elastic architectures and the "pay-as-you-go" resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterize...
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ISBN:
(纸本)9780769551685
Elastic architectures and the "pay-as-you-go" resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterized by high variable workload. In such a context, in-memory transactional data grids have demonstrated to be particularly suited for exploiting advantages provided by elastic computing platforms, mainly thanks to their ability to be dynamically (re-) sized and tuned. Anyway, when specific QoS requirements have to be met, this kind of architectures have revealed to be complex to be managed by humans. Particularly, their management is a very complex task without the stand of mechanisms supporting run-time automatic sizing/tuning of the data platform and the underlying (virtual) hardware resources provided by the cloud. In this paper, we present a neural network-based architecture where the system is constantly and automatically re-configured, particularly in terms of computing resources, in order to achieve transaction class-based QoS while minimizing costs of the infrastructure. We also present some results showing the effectiveness of our architecture, which has been evaluated on top of Future Grid IaaS Cloud using Red Hat Infinispan in-memory data grid and the TPC-C benchmark.
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