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51
Cross-scene crowd counting via deep convolutional neural networks
- In CVPR
"... Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting peo-ple in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd count-ing methods drops significantly when they are applied to an unseen sce ..."
Abstract
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Cited by 2 (1 self)
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scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd count-ing, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better lo-cal optimum for both
Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition
"... This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined hu-man activities. In this problem, extracting effec-tive features for identifying activities is a critica ..."
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problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging
Network In Network
"... We propose a novel deep network structure called “Network In Network”(NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear acti-vation function to scan the input. Instead, we build micro ..."
Abstract
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Cited by 11 (2 self)
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as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local modeling via the micro network, we are able to uti-lize global average pooling over feature maps in the classification layer, which is easier to interpret
Independent Component Analysis and Evolving Fuzzy Neural Networks for the Classification of Single Trial EEG Data
"... The paper presents a novel model for classification of EEG data based on independent component analysis (ICA) as a feature extraction technique, and on evolving fuzzy neural networks - as a classification modeling technique. One of the problems in such models is that some of EEG channels and model v ..."
Abstract
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Cited by 1 (0 self)
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The paper presents a novel model for classification of EEG data based on independent component analysis (ICA) as a feature extraction technique, and on evolving fuzzy neural networks - as a classification modeling technique. One of the problems in such models is that some of EEG channels and model
The 1st International Workshop "Feature Extraction: Modern Questions and Challenges" Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns
"... Abstract Deep neural networks are being widely used for feature representation learning in diverse problem areas ranging from object recognition and speech recognition to robotic perception and human disease prediction. We demonstrate a novel, perhaps the first application of deep learning in mecha ..."
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15 possibilities. We demonstrate that hierarchical feature extraction can potentially lead to a scalable design tool via learning semantic representations from a relatively small number of flow pattern examples. The paper compares the performances of pre-trained deep neural networks and deep
Wireless Sensor Networking in Challenging Environments
, 2014
"... This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington University Open Scholarship. For more information, please contact ..."
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This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington University Open Scholarship. For more information, please contact
Routing and Broadcasting in Ad-Hoc Networks
"... I would like to thank Prof. Dr. Torsten Braun, head of the Computer Network and Distributed Systems group (RVS), for supervising this work and for his insightful advises. Prof. Dr. Torsten Braun encouraged and motivated me to publish my research results and he provided me the opportunity to present ..."
Abstract
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I would like to thank Prof. Dr. Torsten Braun, head of the Computer Network and Distributed Systems group (RVS), for supervising this work and for his insightful advises. Prof. Dr. Torsten Braun encouraged and motivated me to publish my research results and he provided me the opportunity to present
Article TDMA-Based Dual-Mode Communication for Mobile Wireless Sensor Networks
, 2012
"... sensors ..."
Results 1 - 10
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51
