The research of moving objects behavior detection and tracking algorithm in aerial video

2015 ◽  
Author(s):  
Le-le Yang ◽  
Xin Li ◽  
Xiao-ping Yang ◽  
Dong-hui Li
Keyword(s):  
Moving Objects ◽  
Tracking Algorithm ◽  
Behavior Detection ◽  
Detection And Tracking ◽  
Aerial Video

Research on Moving Vehicle Detection and Tracking Algorithm Based on Video Image

2011 ◽  
Vol 467-469 ◽  
pp. 1488-1492 ◽  
Author(s):  
Xin Sha Fu ◽  
Juan Zhu
Keyword(s):  
Difference Method ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Tracking Algorithm ◽  
Moving Vehicle ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Computer Vision Technology ◽  
Background Difference ◽  
Moving Vehicle Detection

Based on the computer vision technology and the digital image processing technology, the video moving vehicle detection and tracking algorithm is made to be on research; with the base of each characters of the background difference method and the inter-frame difference method, a revised comprehensive difference method is used, and combined with the special traffic video background, a background updating method revised from Surrender Algorithm is proposed. The moving object tracking algorithm based on matching matrix is explained to focus on the problem of failure of tracking moving objects when each of them are kept out. The application of software demonstrates that the method cited in this paper proves to be right and feasible and meet the need of highway operation monitor.

Research of Human Body Detection and Tracking Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1023-1027
Author(s):  
Gang Zhang ◽  
Bin Ouyang ◽  
Lu Ming Yu ◽  
Lei Zhang
Keyword(s):  
Human Body ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Histogram Of Oriented Gradient ◽  
Detection And Tracking ◽  
Multi Media ◽  
Teaching Video ◽  
Tracking Technology

In this paper, the proposed algorithm regards the human body object character symbol using Support Vector Machine (SVM) classifier to train and classify Histogram of Oriented Gradient (HOG) features, which improve the accuracy of human body detection. We use optical flow tracking algorithm based on corner points of the contour for tracking. Kalman filter is regarded as the predictor to predict the size and location of the searching object. Also, the size and location of track window is real-time updated. In this paper, we present an object tracking algorithm for multi-media teaching video shoot. Target tracking technology is used for the video image processing analysis. By extracting moving object, we can get information in the subsequent frames to determine the trajectory and size of moving objects. After analysis of a large number of experiments, we can draw the conclusion that the algorithm is effective.

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

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 Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

2014 ◽  
Vol 687-691 ◽  
pp. 564-571 ◽  
Author(s):  
Lin Bao Xu ◽  
Shu Ming Tang ◽  
Jin Feng Yang ◽  
Yan Min Dong
Keyword(s):  
Optical Flow ◽  
Fast Moving ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Robust Tracking ◽  
Symmetry Detection ◽  
Tracking Method ◽  
Robot Tracking ◽  
Improve Accuracy ◽  
First Time

This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.

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