Human Silhouette Extraction Method Using Region Based Background Subtraction

2007 ◽  
pp. 412-420 ◽  
Author(s):  
Jung-Ho Ahn ◽  
Hyeran Byun
Keyword(s):  
Background Subtraction ◽  
Extraction Method ◽  
Human Silhouette ◽  
Silhouette Extraction

scholarly journals FASSD-Net Model for Person Semantic Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1393
Author(s):  
Luis Brandon Garcia-Ortiz ◽  
Jose Portillo-Portillo ◽  
Aldo Hernandez-Suarez ◽  
Jesus Olivares-Mercado ◽  
Gabriel Sanchez-Perez ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Human Interaction ◽  
Video Sequences ◽  
Qualitative And Quantitative ◽  
High Resolution Images ◽  
Quantitative Manner ◽  
High Level ◽  
Time Segmentation ◽  
Human Silhouette ◽  
Silhouette Extraction

This paper proposes the use of the FASSD-Net model for semantic segmentation of human silhouettes, these silhouettes can later be used in various applications that require specific characteristics of human interaction observed in video sequences for the understanding of human activities or for human identification. These applications are classified as high-level task semantic understanding. Since semantic segmentation is presented as one solution for human silhouette extraction, it is concluded that convolutional neural networks (CNN) have a clear advantage over traditional methods for computer vision, based on their ability to learn the representations of appropriate characteristics for the task of segmentation. In this work, the FASSD-Net model is used as a novel proposal that promises real-time segmentation in high-resolution images exceeding 20 FPS. To evaluate the proposed scheme, we use the Cityscapes database, which consists of sundry scenarios that represent human interaction with its environment (these scenarios show the semantic segmentation of people, difficult to solve, that favors the evaluation of our proposal), To adapt the FASSD-Net model to human silhouette semantic segmentation, the indexes of the 19 classes traditionally proposed for Cityscapes were modified, leaving only two labels: One for the class of interest labeled as person and one for the background. The Cityscapes database includes the category “human” composed for “rider” and “person” classes, in which the rider class contains incomplete human silhouettes due to self-occlusions for the activity or transport used. For this reason, we only train the model using the person class rather than human category. The implementation of the FASSD-Net model with only two classes shows promising results in both a qualitative and quantitative manner for the segmentation of human silhouettes.

scholarly journals Extraction of Human Body Using Hybrid Silhouette Extraction Method in Intelligent Robot System

2005 ◽  
Vol 15 (7) ◽  
pp. 852-857
Author(s):  
Moon Hwan Kim ◽  
Young Hoon Joo ◽  
Jin Bae Park ◽  
Young Jo Cho ◽  
Su Young Chi ◽  
...  
Keyword(s):  
Human Body ◽  
Extraction Method ◽  
Intelligent Robot ◽  
Robot System ◽  
Silhouette Extraction

A Novel Approach for Human Silhouette Extraction from Video Data

2017 ◽  
pp. 125-135
Author(s):  
Amlan Raychaudhuri ◽  
Satyabrata Maity ◽  
Amlan Chakrabarti ◽  
Debotosh Bhattacharjee
Keyword(s):  
Video Data ◽  
Novel Approach ◽  
Human Silhouette ◽  
Silhouette Extraction

Human Silhouette Extraction Using Background Modeling and Subtraction Techniques

2007 ◽  
Vol 7 (1) ◽  
pp. 155-159 ◽  
Author(s):  
S. Sulaiman ◽  
A. Hussain ◽  
N. Tahir ◽  
S.A. Samad ◽  
M.M. Mustafa
Keyword(s):  
Background Modeling ◽  
Human Silhouette ◽  
Silhouette Extraction

A Fast-RCNN Implementation for Human Silhouette Detection in Video Sequences

2020 ◽  
Author(s):  
Luis Brandon Garcia-Ortiz ◽  
Gabriel Sanchez-Perez ◽  
Aldo Hernandez-Suarez ◽  
Jesus Olivares-Mercado ◽  
Hector Manuel Perez-Meana ◽  
...  
Keyword(s):  
Fall Detection ◽  
Human Interaction ◽  
Video Sequences ◽  
Surveillance Systems ◽  
Research Areas ◽  
The Common ◽  
Behavioural Biometrics ◽  
Silhouette Detection ◽  
Human Silhouette ◽  
Silhouette Extraction

The intention of this article is to implement a system of detection and segmentation of human silhouettes, the above mentioned tasks present a great challenge in security topics and innovation, in the last years and mainly on automated video surveillance systems, which require understanding the presence and human interaction in video sequences, e.g. Human Computer Interaction (HCI), Human Behaviour comprehension, Human fall detection, among others, but the most important is behavioural biometrics, this paper tackles the common step in these research areas: the Human silhouette extraction through the bounding box. To evaluate the proposed system, standardized databases where used and also proper videos are obtained trying to emulate real-world scenarios, where the quality and the distance are factors that have demonstrated challenges for the detection with computer vision and machine learning.

Robust human silhouette extraction with Laplacian fitting

2014 ◽  
Vol 49 ◽  
pp. 69-76 ◽  
Author(s):  
Somaya Al-Maadeed ◽  
Resheed Almotaeryi ◽  
Richard Jiang ◽  
Ahmed Bouridane
Keyword(s):  
Human Silhouette ◽  
Silhouette Extraction
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