Crowd count in low resolution surveillance video using head detector and color based segementation for disaster management

2015 ◽  
Author(s):  
A.Jaysri Thangam ◽  
Padmini Thupalli Siva ◽  
B. Yogameena
Keyword(s):  
Disaster Management ◽  
Surveillance Video ◽  
Low Resolution

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

Face recognition in low-resolution surveillance video streams

2019 ◽  
Author(s):  
Xuan Zhao ◽  
Yu Chen ◽  
Erik Blasch ◽  
Liwen Zhang ◽  
Genshe Chen
Keyword(s):  
Face Recognition ◽  
Surveillance Video ◽  
Video Streams ◽  
Low Resolution

scholarly journals Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management

2020 ◽  
Vol 10 (2) ◽  
pp. 666 ◽  
Author(s):  
Daekyo Jung ◽  
Vu Tran Tuan ◽  
Dai Quoc Tran ◽  
Minsoo Park ◽  
Seunghee Park
Keyword(s):  
South Korea ◽  
Conceptual Framework ◽  
Natural Disasters ◽  
Disaster Management ◽  
Disaster Response ◽  
Heat Waves ◽  
Fire Detection ◽  
Application Programming Interface ◽  
Cyber Attacks ◽  
Surveillance Video

In order to protect human lives and infrastructure, as well as to minimize the risk of damage, it is important to predict and respond to natural disasters in advance. However, currently, the standardized disaster response system in South Korea still needs further advancement, and the response phase systems need to be improved to ensure that they are properly equipped to cope with natural disasters. Existing studies on intelligent disaster management systems (IDSSs) in South Korea have focused only on storms, floods, and earthquakes, and they have not used past data. This research proposes a new conceptual framework of an IDSS for disaster management, with particular attention paid to wildfires and cold/heat waves. The IDSS uses big data collected from open application programming interface (API) and artificial intelligence (AI) algorithms to help decision-makers make faster and more accurate decisions. In addition, a simple example of the use of a convolutional neural network (CNN) to detect fire in surveillance video has been developed, which can be used for automatic fire detection and provide an appropriate response. The system will also consider connecting to open source intelligence (OSINT) to identify vulnerabilities, mitigate risks, and develop more robust security policies than those currently in place to prevent cyber-attacks.

scholarly journals Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

2021 ◽  
Vol 13 (10) ◽  
pp. 1922
Author(s):  
Lulu Chen ◽  
Yongqiang Zhao ◽  
Jiaxin Yao ◽  
Jiaxin Chen ◽  
Ning Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Spatial Information ◽  
Video Tracking ◽  
Spectral Features ◽  
Surveillance Video ◽  
Tracking Accuracy ◽  
Low Resolution ◽  
Background Clutter ◽  
Appearance Changes

This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.

Gender classification in low-resolution surveillance video: in-depth comparison of random forests and SVMs

2015 ◽  
Author(s):  
Christopher D. Geelen ◽  
Rob G. J. Wijnhoven ◽  
Gijs Dubbelman ◽  
Peter H. N. de With
Keyword(s):  
Random Forests ◽  
Gender Classification ◽  
Surveillance Video ◽  
Low Resolution

Military Involvement in Disaster Preparedness, Organisation, and Disaster Relief — Civilian Military Co-Operation (CIMIC)

2002 ◽  
Vol 17 (S2) ◽  
pp. S25
Author(s):  
Rannveig Bremer Fjær ◽  
Knut Ole Sundnes
Keyword(s):  
Disaster Management ◽  
Disaster Preparedness ◽  
Disaster Relief ◽  
Humanitarian Assistance ◽  
Humanitarian Emergencies ◽  
Military Involvement ◽  
Military Forces

In frequent humanitarian emergencies during the last decades, military forces increasingly have been engaged through provision of equipment and humanitarian assistance, and through peace-support operations. The objective of this study was to evaluate how military resources could be used in disaster preparedness as well as in disaster management and relief.

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