Research on Detection and Tracking of Moving Target in Intelligent Video Surveillance

2012 ◽  
Author(s):  
Lin Lizhong ◽  
Liu Zhiguo ◽  
Zhang Yubin
Keyword(s):  
Video Surveillance ◽  
Moving Target ◽  
Intelligent Video 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.

Multimedia Supported Intelligent Video Surveillance System

2014 ◽  
pp. 34-45
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Real Life ◽  
Multimedia Databases ◽  
Video Surveillance System ◽  
Intelligent Video Surveillance ◽  
Detection And Tracking ◽  
Advanced Search ◽  
Intelligent Video Surveillance System ◽  
Tracking Technology

An integrated multimedia supported intelligent video surveillance system is proposed. The system alleviates the disadvantages of the existing video-surveillance kits and provides advanced search, notification, visualization, and alarming functionality through integration of artificial intelligence, motion detection and tracking technology, multimedia databases, and Internet/cell phone connectivity. The effectiveness and feasibility of the proposed concept is proven through experimental results on a real-life video sequence.

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.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.

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