CNN-based thermal infrared person detection by domain adaptation

2018 ◽  
Author(s):  
Jürgen Beyerer ◽  
Miriam Ruf ◽  
Christian Herrmann
Keyword(s):  
Domain Adaptation ◽  
Thermal Infrared ◽  
Person Detection

Boosting self-worth to buffer the effects of ostracism on person-detection

2014 ◽  
Author(s):  
Jamie L. Gorman ◽  
Kent D. Harber ◽  
Maggie Shiffrar ◽  
Karen Quigley
Keyword(s):  
Person Detection ◽  
Self Worth

Copper-Filled Through-Hole Electrode of a ZnS Window Material for Sealing a Thermal Infrared Sensor

2010 ◽  
Vol 130 (9) ◽  
pp. 437-442
Author(s):  
Takafumi Fukumoto ◽  
Naoki Okamoto ◽  
Yoshimi Ohta ◽  
Yasuhiro Fukuyama ◽  
Masaki Hirota ◽  
...  
Keyword(s):  
Thermal Infrared ◽  
Infrared Sensor ◽  
Window Material ◽  
Through Hole

Domain Adaptation for Visual Recognition

2015 ◽  
Author(s):  
Raghuraman Gopalan ◽  
Ruonan Li ◽  
Vishal M. Patel ◽  
Rama Chellappa
Keyword(s):  
Visual Recognition ◽  
Domain Adaptation

Improving person re-identification performance by customized dataset and person detection

2019 ◽  
Vol 2019 (11) ◽  
pp. 268-1-268-9
Author(s):  
Herman G.J Groot ◽  
Egor Bondarev ◽  
Peter H.N. de With
Keyword(s):  
Identification Performance ◽  
Person Detection

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.

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