About: Multistage Deep Neural Network Framework for People Detection and Localization Using Fusion of Visible and Thermal Images

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About: Multistage Deep Neural Network Framework for People Detection and Localization Using Fusion of Visible and Thermal Images Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Multistage Deep Neural Network Framework for People Detection and Localization Using Fusion of Visible and Thermal Images
Creator	Khan, Muhammad, Usman Khalid, Bushra Akram, Asad
source	PMC
abstract	In Computer vision object detection and classification are active fields of research. Applications of object detection and classification include a diverse range of fields such as surveillance, autonomous cars and robotic vision. Many intelligent systems are built by researchers to achieve the accuracy of human perception but could not quite achieve it yet. Convolutional Neural Networks (CNN) and Deep Learning architectures are used to achieve human like perception for object detection and scene identification. We are proposing a novel method by combining previously used techniques. We are proposing a model which takes multi-spectral images, fuses them together, drops the useless images and then provides semantic segmentation for each object (person) present in the image. In our proposed methodology we are using CNN for fusion of Visible and thermal images and Deep Learning architectures for classification and localization. Fusion of visible and thermal images is carried out to combine informative features of both images into one image. For fusion we are using Encoder-decoder architecture. Fused image is then fed into Resnet-152 architecture for classification of images. Images obtained from Resnet-152 are then fed into Mask-RCNN for localization of persons. Mask-RCNN uses Resnet-101 architecture for localization of objects. From the results it can be clearly seen that Fused model for object localization outperforms the Visible model and gives promising results for person detection for surveillance purposes. Our proposed model gives the Miss Rate of 5.25% which is much better than the previous state of the art method applied on KAIST dataset.
has issue date	2020-06-05(xsd:dateTime)
bibo:doi	10.1007/978-3-030-51935-3_15
has license	no-cc
sha1sum (hex)	f45d225c66e9a57055dc3f8912ba30b770df0ade
schema:url	https://doi.org/10.1007/978-3-030-51935-3_15
resource representing a document's title	Multistage Deep Neural Network Framework for People Detection and Localization Using Fusion of Visible and Thermal Images
has PubMed Central identifier	PMC7340928
schema:publication	Image and Signal Processing
resource representing a document's body	covid:f45d225c66e9a57055dc3f8912ba30b770df0ade#body_text
is schema:about of	named entity 'KAIST' named entity 'Surveillance cameras' named entity 'KAIST' named entity 'wavelength' named entity 'Convolution neural networks' named entity 'object detection' named entity 'Infrared' named entity 'Long Wave' named entity 'feature vector' named entity 'KAIST' named entity 'feature extraction' named entity 'ROI' named entity 'spectral data' named entity 'object detection' named entity 'network architecture' named entity 'KAIST' named entity 'json' named entity 'IOU' named entity 'Infrared' named entity 'CNNs' named entity 'Thermal image' named entity 'object detection' named entity 'crime prevention' named entity 'thermal cameras' named entity 'spectral data' named entity 'ground truth' named entity 'KAIST' named entity 'pixel by pixel' named entity 'infrared' named entity 'thermal cameras' named entity 'ResNet' named entity 'bounding box' named entity 'Object detection' named entity 'CNNs' named entity 'bounding box' named entity 'LWIR' named entity 'data set' named entity 'data set' named entity 'RGB' named entity 'InfraRed' named entity 'bounding box' named entity 'object detection' named entity 'ROI' named entity 'object detection' named entity 'NIR' named entity 'MWIR' named entity 'Thermal images' named entity 'object detection' named entity 'pixel' named entity 'Infrared' named entity 'LWIR' named entity 'spectral data' named entity 'infrared' named entity 'ResNet' named entity 'MWIR' named entity 'CCTV' named entity 'object detection' named entity 'data set' named entity 'spectral data' named entity 'Convolutional Neural Network' named entity 'CNN' named entity 'KAIST' named entity 'NVIDIA' named entity 'fog' named entity 'convolution' named entity 'infrared images' named entity 'CCTV cameras' named entity 'convolution' named entity 'data set' named entity 'LWIR' named entity 'surveillance cameras' named entity 'loss function' named entity 'GPU' named entity 'human perception' named entity 'CNN' named entity 'IOU' named entity 'Object detection' named entity 'data sets' named entity 'image data' named entity 'object detection'

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