The motion detection method apply to video-surveillance embedded system

2011 ◽  
Author(s):  
Lili Dai ◽  
Yuye Wang ◽  
Jingwen Wang
Keyword(s):  
Embedded System ◽  
Video Surveillance ◽  
Motion Detection ◽  
Detection Method

Detection of Human Body Motion Based on Electrostatic Measurements in the Indoor Environment

2011 ◽  
Vol 480-481 ◽  
pp. 1329-1334
Author(s):  
Wei Zheng ◽  
Zhan Zhong Cui
Keyword(s):  
Theoretical Analysis ◽  
Real Time ◽  
Human Body ◽  
Motion Detection ◽  
Average Velocity ◽  
Indoor Environment ◽  
Detection Method ◽  
Body Motion ◽  
The Real ◽  
Multiple Electrodes

An effective non-contact electrostatic detection method is used for human body motion detection. Theoretical analysis and pratical experiments are carried out to prove that this method is effective in the field of human body monitoring, in which a model for human body induced potential by stepping has been proposed. Furthermore, experiment results also prove that it’s feasible to measure the average velocity and route of human body motion by multiple electrodes array. What’s more the real-time velocity and direction of human body motion can be determined by orthogonal electrostatic detector array, and the real-time velocity and direction of human body motion can be obtained within the range of 2 meters.

Abnormal Motion Detection for Intelligent Video Surveillance

2011 ◽  
Vol 58-60 ◽  
pp. 2290-2295 ◽  
Author(s):  
Ruo Hong Huan ◽  
Xiao Mei Tang ◽  
Zhe Hu Wang ◽  
Qing Zhang Chen
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Video Surveillance ◽  
Motion Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Video Streams ◽  
Intelligent Video Surveillance ◽  
Tracking Process ◽  
Object Parameters

A method of abnormal motion detection for intelligent video surveillance is presented, which includes object intrusion detection, object overlong stay detection and object overpopulation detection. Background subtraction algorithm is used to detect moving objects in video streams. Kalman filter is applied for object tracking. By the construction of relation matrix, the tracking process is divided into five statuses for prediction and estimation, which are object disappearing, object separating, new object appearing, object sheltering and object matching. The object parameters and predictive information in the next frame which is used to track moving objects is established by Kalman filter. Then, three types of abnormal motion detection are implemented. The relative position of alarm area or guard line with the rectangle boxes of the moving objects is used to detect whether the object is invading. The existing time of the moving objects in monitor area is counted to detect whether the object is staying too long. Moving objects in the monitor area are classified and counted to detect whether the objects are too much. Alarm will be triggered when abnormal motion detection as defined is detected in the monitor area.

Based Embedded System Applications in Intelligent Home Remote Monitoring

2014 ◽  
Vol 602-605 ◽  
pp. 2317-2320
Author(s):  
Yang Li ◽  
Qing Hong Wu ◽  
Xue Xiao
Keyword(s):  
Embedded System ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Video Capture ◽  
Home Video ◽  
Intelligent Home ◽  
Front End ◽  
Usb Camera

With the continuous improvement of security awareness, home security has become the focus of attention. The actual demand for home video surveillance system, designed a cheap, practical, small size and low power consumption of video surveillance systems, this paper uses microprocessor S3C2440 ARM9 core as the core hardware control, embedded Linux operating system with software the control core, and cheap, generic USB camera video capture device as a front end to complete the design of a home video surveillance system.

scholarly journals A comparative study of motion detection with FMG and sEMG methods for assistive applications

2020 ◽  
Vol 7 ◽  
pp. 205566832093858
Author(s):  
Muhammad Raza Ul Islam ◽  
Asim Waris ◽  
Ernest Nlandu Kamavuako ◽  
Shaoping Bai
Keyword(s):  
Motion Detection ◽  
Classification Accuracy ◽  
Detection Method ◽  
Daily Basis ◽  
Detection Methods ◽  
Feed Forward Neural Network ◽  
Training Scheme ◽  
Training Schemes ◽  
And Training

Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.

Design on Intelligent Video Surveillance System Based on Target Identification Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 460-465 ◽  
Author(s):  
Zong Jie Meng ◽  
Cai Jie
Keyword(s):  
Video Surveillance ◽  
Motion Detection ◽  
Target Identification ◽  
Identification Algorithm ◽  
Accuracy Rate ◽  
Intelligent Video Surveillance ◽  
Frame Difference ◽  
Adjacent Frame ◽  
Intelligent Video Surveillance System ◽  
Hardware Realization

This paper makes study on the adjacent frame difference and algorithm realization of SOM i8dentification after improvement, of which it includes motion detection, target identification; the realized video surveillance module makes up the intelligent video surveillance that can reconstruct platform. Motion detection module adopts algorithm of adjacent frame difference after improvement, which can correctly mark the motion object. Target identification module adopts self-mapping nerve net after improvement, it is easier for hardware realization, and meanwhile the accuracy rate of identification is equal to classical algorithm.

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