scholarly journals Robust Descriptors Fusion for Pedestrians’ Re-identification and Tracking Across a Camera Network

2013 ◽  
Keyword(s):  
Camera Network

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

scholarly journals Continuous Graph Partitioning for Camera Network Surveillance*

2012 ◽  
Vol 45 (26) ◽  
pp. 228-233 ◽  
Author(s):  
D. Borra ◽  
F. Pasqualetti ◽  
F. Bullo
Keyword(s):  
Graph Partitioning ◽  
Camera Network ◽  
Network Surveillance

scholarly journals A Scalable Platform for Distributed Object Tracking across a Many-camera Network

2021 ◽  
pp. 1-1
Author(s):  
Aakash Digambar Khochare ◽  
Aravindhan Krishnan ◽  
Yogesh Simmhan
Keyword(s):  
Object Tracking ◽  
Camera Network ◽  
Distributed Object

Tracklet style transfer and part-level feature description for person reidentification in a camera network

2021 ◽  
Author(s):  
Yosra Dorai ◽  
Sami Gazzah ◽  
Frederic Chausse ◽  
Najoua Essoukri Ben Amara
Keyword(s):  
Camera Network ◽  
Style Transfer ◽  
Feature Description

Calibrating a wide-area camera network with non-overlapping views using mobile devices

2014 ◽  
Vol 10 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Thomas Kuo ◽  
Zefeng Ni ◽  
Santhoshkumar Sunderrajan ◽  
B. S. Manjunath
Keyword(s):  
Mobile Devices ◽  
Wide Area ◽  
Camera Network

Toward low latency gesture control using smart camera network

2008 ◽  
Author(s):  
Zoran Zivkovic ◽  
Vitaly Kliger ◽  
Richard Kleihorst ◽  
Alexander Danilin ◽  
Ben Schueler ◽  
...  
Keyword(s):  
Low Latency ◽  
Smart Camera ◽  
Camera Network ◽  
Gesture Control

IP Camera Based Network Video Surveillance Mobile Security System

2013 ◽  
Vol 411-414 ◽  
pp. 1505-1509
Author(s):  
Long Chen ◽  
Shuan Chen ◽  
Yu Qing He ◽  
Jian Guo Wei ◽  
Jian Wu Dang
Keyword(s):  
Video Surveillance ◽  
Depth Estimation ◽  
Mobile Security ◽  
Security System ◽  
Camera Network ◽  
Monitoring Methods ◽  
Mobile Client ◽  
Alarm Signal ◽  
Cloud Server ◽  
Ip Camera

In this paper, we proposed a mobile security system based on IP camera network video surveillance by improving the traditional monitoring methods. This system includes three parts: Android mobile client, cloud server, IP camera monitoring terminals. By setting IP cameras at 24 fps but only sending one frame each second, we can infer the situation by analyzing of the single image and reduce the amount of data transmission. User can check the live scenes and send control commands to monitoring terminal or receive the push information from the server to freely give alarm signal to users anytime, anywhere. And users can also select any regions or any specific objects to monitor, the server will real-time monitor each scene according to the specific requirements by using the algorithms of image contrast and depth estimation. The experiment results show that the system can greatly improve the accuracy of the alarm.

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