Multimodal person detection system

2021 ◽  
Author(s):  
Philip Barello ◽  
Md Shafaeat Hossain
Keyword(s):  
Detection System ◽  
Person Detection

New Swarm Intelligence Technique of Artificial Social Cockroaches for Suspicious Person Detection Using N-Gram Pixel With Visual Result Mining

2019 ◽  
pp. 545-570
Author(s):  
Hadj Ahmed Bouarara ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Detection System ◽  
Private Life ◽  
Graphical Form ◽  
Surveillance Video ◽  
Insect Behaviour ◽  
Public And Private ◽  
Person Detection ◽  
Surveillance Camera ◽  
N Gram ◽  
Representation Method

In the last decade, surveillance camera technology has become widely practiced in public and private places to ensure the safety of individuals. Merely, face to limits of violation the private life of people and the inability to identify malicious persons that hid their faces, finding a new policy of surveillance video has become compulsory. The authors' work deals on the development of a suspicious person detection system using a new insect behaviour algorithm called artificial social cockroaches ASC based on a new image representation method (n-gram pixel). It has as input a set of artificial cockroaches (human images) to classify them (hide) into shelters (classes) suspicious or normal depending on a set of aggregation rules (shelter darkness, congener's attraction and security quality). Their experiments were performed on a modified MuHAVi dataset and using the validation measures (recall, precision, f-measure, entropy and accuracy), in order to show the benefit derived from using such approach compared to the result of classical algorithms (KNN and C4.5). Finally, a visualisation step was achieved to see the results in graphical form with more realism for the purpose to help policeman, security associations and justice in their investigation.

scholarly journals Online Multi-Layers Social Distance Detection in Iraqi Schools

2021 ◽  
Vol 12 (6) ◽  
pp. 2103-2108
Author(s):  
Abdulhakeem Q. Albayati, Farah F. Alkhalid, Rafah K. Hussain
Keyword(s):  
Social Distance ◽  
Detection System ◽  
Main Idea ◽  
High Accuracy ◽  
Epidemic Spread ◽  
Health Organizations ◽  
Person Detection ◽  
Whole School ◽  
School Yard ◽  
Better Than

In the circumstance of the COVID-19 pandemic, Prevention is better than cure, especially if the cure is not available, the first motto that all health organizations recommend is keep distances between people to prevent epidemic spread. In this paper, an online multi layers social distance detection system is proposed, the main idea is to detect distance among pupils and classify the distance to accept or not, this system treats stream video of fixed camera which monitor the whole school yard where the pupils are available, this proposed system used multi layers, the first is to make person detection using Yolo-4 approach including CNN model, and surround it by rectangle, the second is to specify the center of detected person, finally, calculate the relative distance to decide if it is accepted or not, this system works online and give high accuracy.

2A1-S03 Development of a person detection system using high-speed vision

2015 ◽  
Vol 2015 (0) ◽  
pp. _2A1-S03_1-_2A1-S03_4
Author(s):  
Ryuji OKADA ◽  
Tadashi YOSHIDOME ◽  
Noriyuki KAWARAZAKI
Keyword(s):  
High Speed ◽  
Detection System ◽  
Person Detection

scholarly journals Comparison of FairMOT-VGG16 and MCMOT Implementation for Multi-Object Tracking and Gender Detection on Mall CCTV

2021 ◽  
Vol 14 (1) ◽  
pp. 49-64
Author(s):  
Pray Somaldo ◽  
Dina Chahyati
Keyword(s):  
Detection System ◽  
Tracking System ◽  
Shopping Center ◽  
Specific Information ◽  
Gender Classification ◽  
Person Detection ◽  
Men And Women ◽  
Multi Person Tracking ◽  
And Gender ◽  
Gender Detection

The crowd detection system on CCTV has proven to be useful for retail and shopping sector owners in mall areas. The data can be used as a guide by shopping center owners to find out the number of visitors who enter at a certain time. However, such information was still insufficient. The need for richer data has led to the development of more specific person detection which involves gender. Gender detection can provide specific information on the number of men and women visiting a particular location. However, gender detection alone does not provide an identity label for every detection that occurs, so it needs to be combined with a multi-person tracking system. This study compares two tracking methods with gender detection, namely FairMOT with gender classification and MCMOT. The first method produces MOTA, MOTP, IDS, and FPS of 78.56, 79.57, 19, and 24.4, while the second method produces 69.84, 81.94, 147, and 30.5. In addition, evaluation of gender was also carried out where the first method resulted in a gender accuracy of 65\% while the second method was 62.35\%. 

scholarly journals New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5848
Author(s):  
Mohamed Chouai ◽  
Petr Dolezel ◽  
Dominik Stursa ◽  
Zdenek Nemec
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Human Head ◽  
Semantic Segmentation ◽  
Person Detection ◽  
Head Detection ◽  
Bounding Boxes ◽  
Global Accuracy ◽  
Private Datasets ◽  
Safety Systems

In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.

New Swarm Intelligence Technique of Artificial Social Cockroaches for Suspicious Person Detection Using N-Gram Pixel With Visual Result Mining

2019 ◽  
pp. 830-856
Author(s):  
Hadj Ahmed Bouarara ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Detection System ◽  
Private Life ◽  
Graphical Form ◽  
Surveillance Video ◽  
Insect Behaviour ◽  
Public And Private ◽  
Person Detection ◽  
Surveillance Camera ◽  
N Gram ◽  
Representation Method

In the last decade, surveillance camera technology has become widely practiced in public and private places to ensure the safety of individuals. Merely, face to limits of violation the private life of people and the inability to identify malicious persons that hid their faces, finding a new policy of surveillance video has become compulsory. The authors' work deals on the development of a suspicious person detection system using a new insect behaviour algorithm called artificial social cockroaches ASC based on a new image representation method (n-gram pixel). It has as input a set of artificial cockroaches (human images) to classify them (hide) into shelters (classes) suspicious or normal depending on a set of aggregation rules (shelter darkness, congener's attraction and security quality). Their experiments were performed on a modified MuHAVi dataset and using the validation measures (recall, precision, f-measure, entropy and accuracy), in order to show the benefit derived from using such approach compared to the result of classical algorithms (KNN and C4.5). Finally, a visualisation step was achieved to see the results in graphical form with more realism for the purpose to help policeman, security associations and justice in their investigation.

scholarly journals Intelligent masked-person detection system for epidemic prevention and control

2021 ◽  
Vol 336 ◽  
pp. 06005
Author(s):  
Yizhuo Zhou ◽  
Jiming Sa ◽  
Yang Xiang ◽  
Yihao Zhang ◽  
Fenghao Zheng ◽  
...  
Keyword(s):  
Face Detection ◽  
Prevention And Control ◽  
Detection System ◽  
Experimental Results ◽  
Person Detection ◽  
Model Training ◽  
And Control

In order to control the epidemic and complete the supervision of increasing population, we devised a kind of face detection system. This system detected face with MTCNN and then it detect whether the person wears the mask with MobileNet. Also we added non-standardized samples in the model training so that it can detect pedestrians who are not properly worn. The experimental results showed that the system can effectively identify the wearing of masks.

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