Research of Moving Target Tracking Algorithms in Video Surveillance System

2017 ◽  
pp. 192-197
Author(s):  
Xu Lei ◽  
Peng Yueping ◽  
Liu Man
Keyword(s):  
Target Tracking ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Target ◽  
Video Surveillance System ◽  
Moving Target Tracking ◽  
Tracking Algorithms

A Survey on Moving Target Tracking in the Intelligent Visual Monitoring System

2014 ◽  
Vol 599-601 ◽  
pp. 790-793 ◽  
Author(s):  
Meng Xin Li ◽  
Gao Ling Su ◽  
Jing Hou ◽  
Dai Zheng
Keyword(s):  
Target Tracking ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
Visual Surveillance ◽  
Moving Target ◽  
Extended Kalman Filtering ◽  
Moving Target Tracking ◽  
Filtering Algorithm ◽  
Tracking Algorithms ◽  
Kalman Filtering Algorithm

Moving target tracking is the key part of intelligent visual surveillance system. Among the various tracking algorithms, the Beysian tracking algorithms and the kernel tracking algorithm are two algorithms that frequently used. The Beysian tracking algorithms mainly conclude Kalman filtering algorithm, extended Kalman filtering algorithm and particle filtering algorithm. Mean Shift is the most representative algorithm of the kernel target tracking. In this survey, the status and development of target tracking algorithms has been studied more extensively with providing a few examples of modified tracking algorithms. Then a comparison was presented based on the limitations and scope of applications. Finally, the paper showed further research prospects of moving target tracking are introduced.

The Realization of Moving Target Tracking

2013 ◽  
Vol 389 ◽  
pp. 770-775
Author(s):  
Guang Long Li ◽  
Xiang Bin Zhu ◽  
Hai Geng
Keyword(s):  
Computer Vision ◽  
Target Tracking ◽  
Open Source ◽  
Real Time ◽  
Target Detection ◽  
Video Surveillance ◽  
Target Object ◽  
Moving Target ◽  
Moving Target Tracking ◽  
Detection And Recognition

This paper is mainly talking about processing, analysis and understanding of video signal in intelligent video surveillance, and we designed an efficient target detection and recognition model. Through the model to detect the target object in motion and then track the detected target. Eventually we achieved real-time monitoring of the unguarded areas. The focus of this article is about how to achieve the moving target tracking in OpenCV (Open Source Computer Vision Library) environment.

Probabilistic model single target tracking framework for video surveillance system

2015 ◽  
Vol 23 (7) ◽  
pp. 2093-2099
Author(s):  
李静宇 LI Jing-yu ◽  
刘艳滢 LIU Yan-ying ◽  
田睿 TIAN Rui ◽  
王延杰 WANG Yan-jie ◽  
姜瑞凯 JIANG Rui-kai
Keyword(s):  
Target Tracking ◽  
Video Surveillance ◽  
Probabilistic Model ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Single Target ◽  
Single Target Tracking

Moving Objects Detection and Speed Estimation in Intelligent Surveillance System

2013 ◽  
Vol 712-715 ◽  
pp. 2354-2358
Author(s):  
Zhi Hong Xi ◽  
Guang Hui Dong
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Operating Conditions ◽  
Background Model ◽  
Moving Target ◽  
Video Surveillance System ◽  
Target Area ◽  
Intelligent Video Surveillance ◽  
Intelligent Video Surveillance System

In order to solve the problem of highway intelligent video surveillance system for effective monitoring of vehicle operating conditions, a fast block background modeling method is proposed in the framework for intelligent video surveillance system. First using statistical histogram to build the background model of the video surveillance system, second using background subtraction method to locate the moving target area, at last using displacement of the minimum exterior rectangle centroid of the moving target between two frames to calculate moving target speed, without the aid calibration. Experimental results show that the proposed method exhibits its superiority in processing time, the time of building background model through 100 frames is 3.8s. The proposed method has good practical value used in intelligent video surveillance.

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