Energy-Efficient Mutual Authentication Protocol for Handhled Devices Based on Public Key Cryptography

Khan, Wilayat; Ullah, Habib; Hussain, Riaz

doi:10.7763/ijcte.2013.v5.790

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…After computing optical flow for whole video segment, the next step is to generate dense and long trajectories [43], [44]. We overlay grid of particles over the first optical flow field.…”

Section: Particle Advectionmentioning

confidence: 99%

Identifying Human Behavious Using Deep Trajectory Descriptors

Taghizadehghalehjoughi¹,

Alreshidi²

2019

Preprint

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Identifying human actions in complex scenes is widely considered as a challenging research problem due to the unpredictable behaviors and variation of appearances and postures. For extracting variations in motion and postures, trajectories provide meaningful way. However, simple trajectories are normally represented by vector of spatial coordinates. In order to identify human actions, we must exploit structural relationship between different trajectories. In this paper, we propose a method that divides the video into N number of segments and then for each segment we extract trajectories. We then compute trajectory descriptor for each segment which capture the structural relationship among different trajectories in the video segment. For trajectory descriptor, we project all extracted trajectories on the canvas. This will result in texture image which can store the relative motion and structural relationship among the trajectories. We then train Convolution Neural Network (CNN) to capture and learn the representation from dense trajectories. . Experimental results shows that our proposed method out performs state of the art methods by 90.01% on benchmark data set.

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“…After computing optical flow for whole video segment, the next step is to generate dense and long trajectories [43], [44]. We overlay grid of particles over the first optical flow field.…”

Section: Particle Advectionmentioning

confidence: 99%

Identifying Human Behavious Using Deep Trajectory Descriptors

Taghizadehghalehjoughi¹,

Alreshidi²

2019

Preprint

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“…After computing optical flow, the next step is to generate dense and long trajectories based on the optical flow [43], [44]. For doing so, we overlay grid of particles over the first optical flow field where each initial location (horizontal and vertical location) of the particle represents the source point.…”

Section: Particle Advectionmentioning

confidence: 99%

Characterizing Human Behaviours Using Statistical Motion Descriptor

Alreshidi¹,

Bilal²

2019

SIPIJ

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Identifying human behaviors is a challenging research problem due to the complexity and variation of appearances and postures, the variation of camera settings, and view angles. In this paper, we try to address the problem of human behavior identification by introducing a novel motion descriptor based on statistical features. The method first divide the video into N number of temporal segments. Then for each segment, we compute dense optical flow, which provides instantaneous velocity information for all the pixels. We then compute Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32 bins. We then compute statistical features from the obtained HOOF forming a descriptor vector of 192dimensions. We then train a non-linear multi-class SVM that classify different human behaviors with the accuracy of 72.1%. We evaluate our method by using publicly available human action data set. Experimental results shows that our proposed method out performs state of the art methods.

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“…This leads researchers to conduct studies and review various crowd density estimation approaches ( Zhang et al, 2016b ). Researchers have stated that the most stable and effective method of estimating multiple densities in comparison to detection-based methods is texture-based analysis ( Ullah & Conci, 2012 ; Ullah et al, 2010 ; Khan & Ullah, 2010 ; Uzair et al, 2009 ; Khan, Ullah & Hussain, 2013 ; Saqib, Khan & Blumenstein, 2016 ; Saleh, Suandi & Ibrahim, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network

Bhuiyan

Abdullah

Hashim

et al. 2022

PeerJ Computer Science

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This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully convolutional neural network (FCNN)-based method to monitor crowd analysis, especially for the classification of crowd density. This study aims to address the current technological challenges faced in video analysis in a scenario where the movement of large numbers of pilgrims with densities ranging between 7 and 8 per square meter. To address this challenge, this study aims to develop a new dataset based on the Hajj pilgrimage scenario. To validate the proposed method, the proposed model is compared with existing models using existing datasets. The proposed FCNN based method achieved a final accuracy of 100%, 98%, and 98.16% on the proposed dataset, the UCSD dataset, and the JHU-CROWD dataset, respectively. Additionally, The ResNet based method obtained final accuracy of 97%, 89%, and 97% for the proposed dataset, UCSD dataset, and JHU-CROWD dataset, respectively. The proposed Hajj-Crowd-2021 crowd analysis dataset and the model outperformed the other state-of-the-art datasets and models in most cases.

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Energy-Efficient Mutual Authentication Protocol for Handhled Devices Based on Public Key Cryptography

Cited by 8 publications

References 11 publications

Identifying Human Behavious Using Deep Trajectory Descriptors

Identifying Human Behavious Using Deep Trajectory Descriptors

Characterizing Human Behaviours Using Statistical Motion Descriptor

A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network

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