Fusion of Motion and Appearance for Robust People Detection in Cluttered Scenes

2011 ◽  
pp. 111-124
Author(s):  
Jianguo Zhang ◽  
Shaogang Gong
Keyword(s):  
People Detection

The Special Features of Radar Developments for People Detection under Obstructions

2004 ◽  
Vol 61 (7-12) ◽  
pp. 875-893 ◽  
Author(s):  
I. A. Vyazmitinov ◽  
Ye. I. Myroshnychenko ◽  
O. V. Sytnik
Keyword(s):  
People Detection

scholarly journals Smart Video Surveillance System Based on Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2958
Author(s):  
Antonio Carlos Cob-Parro ◽  
Cristina Losada-Gutiérrez ◽  
Marta Marrón-Romera ◽  
Alfredo Gardel-Vicente ◽  
Ignacio Bravo-Muñoz
Keyword(s):  
Computer Vision ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Smart Camera ◽  
People Detection ◽  
Camera System ◽  
Edge Node ◽  
New Processing ◽  
Processor Unit

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.

Applications for a people detection and tracking algorithm using a time-of-flight camera

2014 ◽  
Vol 75 (17) ◽  
pp. 10769-10786 ◽  
Author(s):  
Carsten Stahlschmidt ◽  
Alexandros Gavriilidis ◽  
Jörg Velten ◽  
Anton Kummert
Keyword(s):  
Time Of Flight ◽  
Tracking Algorithm ◽  
People Detection ◽  
Detection And Tracking

Robust people detection using depth information from an overhead Time-of-Flight camera

2017 ◽  
Vol 71 ◽  
pp. 240-256 ◽  
Author(s):  
Carlos A. Luna ◽  
Cristina Losada-Gutierrez ◽  
David Fuentes-Jimenez ◽  
Alvaro Fernandez-Rincon ◽  
Manuel Mazo ◽  
...  
Keyword(s):  
Time Of Flight ◽  
Depth Information ◽  
People Detection

Multi-Robot Multiple Camera People Detection and Tracking in Automated Warehouses

2021 ◽  
Author(s):  
Michela Zaccaria ◽  
Mikhail Giorgini ◽  
Riccardo Monica ◽  
Jacopo Aleotti
Keyword(s):  
People Detection ◽  
Multiple Camera ◽  
Detection And Tracking ◽  
Automated Warehouses ◽  
Multi Robot

Community Structure Detection Using Firefly Algorithm

2018 ◽  
Vol 9 (4) ◽  
pp. 52-70 ◽  
Author(s):  
Ameera Saleh Jaradat ◽  
Safa'a Bani Hamad
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Graph Partitioning ◽  
Firefly Algorithm ◽  
Real Life ◽  
People Detection ◽  
Continuous Growth ◽  
Structure Detection ◽  
Partitioning Problem ◽  
And Function

This article describes how parallel to the continuous growth of the Internet, which allows people to share and collaborate more, social networks have become more attractive as a research topic in many different disciplines. Community structures are established upon interactions between people. Detection of these communities has become a popular topic in computer science. How to detect the communities is of great importance for understanding the organization and function of networks. Community detection is considered a variant of the graph partitioning problem which is NP-hard. In this article, the Firefly algorithm is used as an optimization algorithm to solve the community detection problem by maximizing the modularity measure. Firefly algorithm is a new Nature-inspired heuristic algorithm that proved its good performance in a variety of applications. Experimental results obtained from tests on real-life networks demonstrate that the authors' algorithm successfully detects the community structure.

People Detection and Tracking Using an On-Board Drone Camera

2019 ◽  
pp. 668-680
Author(s):  
Cristian Cifuentes-García ◽  
Daniel González-Medina ◽  
Ismael García-Varea
Keyword(s):  
People Detection ◽  
Detection And Tracking
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