1,720,987 research outputs found
A Mathematical Programming Approach to Task Offloading in Visual Sensor Networks
Full text linkThis work studies how visual analysis tasks based on feature extraction can be speeded up in the context of Visual Sensor Networks. The main catch is for the camera node to leverage the presence of neighboring sensor nodes and offload the task, thus parallelizing its execution. We propose two mathematical programming formulations for the optimal visual task offloading problem: the first one targets the minimization of the overall task completion time while enforcing energy consumption constraints onto the nodes; the second maximizes the overall sensor network lifetime subject to a temporal constraint on the task completion time. The aforementioned formulations are used to characterize the achievable speed-up and consequent energy consumption in representative visual sensor network topologies
Compress-then-analyze vs. analyze-then-compress: Two paradigms for image analysis in visual sensor networks
Full text linkWe compare two paradigms for image analysis in vi- sual sensor networks (VSN). In the compress-then-analyze (CTA) paradigm, images acquired from camera nodes are compressed and sent to a central controller for further analysis. Conversely, in the analyze-then-compress (ATC) approach, camera nodes perform visual feature extraction and transmit a compressed version of these features to a central controller. We focus on state-of-the-art binary features which are particularly suitable for resource-constrained VSNs, and we show that the ”winning” paradigm depends primarily on the network conditions. Indeed, while the ATC approach might be the only possible way to perform analysis at low available bitrates, the CTA approach reaches the best results when the available bandwidth enables the transmission of high-quality images
Performance evaluation of object recognition tasks in visual sensor networks
No full textWe analyze the performance of TCP and UDP transport protocols when applied to image retrieval or object recognition in wireless visual sensor networks (VSN). We focus on two different paradigms for image analysis, namely compress-then-analyze (CTA) and analyze-then-compress (ATC). The former entails the transmission of JPEG encoded images from camera nodes to a server, where the analysis takes place. The latter consists in extracting and compressing local visual features on board camera nodes, before transmission to a remote location. The presented analysis is useful to assess the best coupling between application and transport layers under delay and accuracy constraints for different networking conditions
Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting
Full text linkA robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost)
Compress-then-Analyze vs Analyze-then-Compress: what is best in Visual Sensor Networks?
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