Motion detection and tracking using belief indicators for an automatic visual-surveillance system

2006 ◽  
Vol 24 (11) ◽  
pp. 1192-1201 ◽  
Author(s):  
Cina Motamed
Keyword(s):  
Motion Detection ◽  
Surveillance System ◽  
Visual Surveillance ◽  
Detection And Tracking

Study on Tracking of Moving Object in Intelligent Video Surveillance System

2012 ◽  
Vol 433-440 ◽  
pp. 6583-6588 ◽  
Author(s):  
Ping Guang Cheng ◽  
Jian Hua Yong
Keyword(s):  
Video Surveillance ◽  
Motion Detection ◽  
Surveillance System ◽  
Video Tracking ◽  
Practical Application ◽  
Intelligent Video Surveillance ◽  
Original Algorithm ◽  
Detection And Tracking ◽  
Intelligent Video Surveillance System ◽  
Depth Study

Through the in-depth study of the current motion detection and tracking technologies, combined with the practical application of intelligent video surveillance, this paper improves the existing motion detection and tracking algorithm. The improved algorithm continues the characteristics of original algorithm such as simple to implement and lower computational complexity, increases its range of application, and improves the anti-jamming capability and robustness of video tracking.

scholarly journals Robust Real Time Multiple Human Detection and Tracking for Automatic Visual Surveillance System

2021 ◽  
Vol 17 ◽  
pp. 93-98
Author(s):  
LAKHYADEEP KONWAR ◽  
ANJAN KUMAR TALUKDAR ◽  
KANDARPA KUMAR SARMA
Keyword(s):  
Particle Filter ◽  
Surveillance System ◽  
Graph Cut ◽  
Visual Surveillance ◽  
Human Detection ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Tracking Accuracy ◽  
Detection And Tracking ◽  
Automation Systems

Detection of human for visual surveillance system provides most important rule for advancement in the design of future automation systems. Human detection and tracking are important for future automatic visual surveillance system (AVSS). In this paper we have proposed a flexible technique for proper human detection and tracking for the design of AVSS. We used graph cut for segment human as a foreground image by eliminating background, extract some feature points by using HOG, SVM classifier for proper classification and finally we used particle filter for tracking those of detected human. Our system can easily detect and track humans in poor lightening conditions, color, size, shape, and clothing due to the use of HOG feature descriptor and particle filter. We use graph cut based segmentation technique, therefore our system can handle occlusion at about 88%. Due to the use of HOG to extract features our system can properly work in indoor as well as outdoor environments with 97.61% automatic human detection and 92% automatic human detection and tracking accuracy of multiple human

DEWS: A Live Visual Surveillance System for Early Drowning Detection at Pool

2008 ◽  
Vol 18 (2) ◽  
pp. 196-210 ◽  
Author(s):  
How-Lung Eng ◽  
Kar-Ann Toh ◽  
Wei-Yun Yau ◽  
Junxian Wang
Keyword(s):  
Surveillance System ◽  
Visual Surveillance

Motion Detection of Webcam Using Frame Differencing Method

2021 ◽  
pp. 32-38
Author(s):  
Pooja Nagpal ◽  
Shalini Bhaskar Bajaj ◽  
Aman Jatain ◽  
Sarika Chaudhary
Keyword(s):  
Machine Learning ◽  
Motion Detection ◽  
Background Subtraction ◽  
Surveillance System ◽  
Detection Algorithm ◽  
Alert Message ◽  
Object Level

It is the capability of humans and as well as vehicles to automatically detect object level motion that results into collision less navigation and also provides sense of situation. This paper presents a technique for secure object level motion detection which yields more accurate results. To achieve this, python code has been used along with various machine learning libraries. The detection algorithm uses the advantage of background subtraction and fed in data to detect even the slightest movement this system makes use of a webcam to scan a premise and detect movement of any sort; on the recognition of any activity it immediately sends an alert message to the owner of the system via mail. Any person requiring a surveillance system can use it.

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