Journal ArticleDOI
Object-oriented convolutional features for fine-grained image retrieval in large surveillance datasets
TLDR
An object-oriented feature selection mechanism for deep convolutional features from a pre-trained CNN that achieves better precision and recall than the full feature set for objects with varying backgrounds and reduces number of feature maps without performance degradation.About:
This article is published in Future Generation Computer Systems.The article was published on 2018-04-01. It has received 24 citations till now. The article focuses on the topics: Feature (computer vision) & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
TL;DR: This paper proposes an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire, which uses smaller convolutional kernels and contains no dense, fully connected layers.
Journal ArticleDOI
Efficient Fire Detection for Uncertain Surveillance Environment
TL;DR: Considering the accuracy, false alarms, size, and running time of the proposed CNN based system, it is believed that it is a suitable candidate for fire detection in uncertain IoT environment for mobile and embedded vision applications during surveillance.
Journal ArticleDOI
Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM
TL;DR: A framework for activity recognition in surveillance videos captured over industrial systems is proposed and the results reveal the effectiveness of the proposed method for activity Recognition in industrial settings compared with state-of-the-art methods.
Journal ArticleDOI
Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM
Tanveer Hussain,Khan Muhammad,Amin Ullah,Zehong Cao,Sung Wook Baik,Victor Hugo C. de Albuquerque +5 more
TL;DR: This article achieves MVS by integrating deep neural network based soft computing techniques in a two-tier framework that extracts deep features from each frame of a sequence in the lookup table and passes them to deep bidirectional long short-term memory (DB-LSTM) to acquire probabilities of informativeness and generates a summary.
Journal ArticleDOI
A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication
TL;DR: This paper presents a novel authentication system using an efficient feature detection algorithm and a convolutional neural network (CNN) based on ECG for human authentication that is highly usable in a real-time authentication system.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.