scholarly journals A Fast Moving Object Tracking Method by the Combination of Covariance Matrix and Kalman Filter Algorithm

2015 ◽  
Vol 19 (6) ◽  
pp. 1477-1484 ◽  
Author(s):  
Geum-boon Lee
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Covariance Matrix ◽  
Fast Moving ◽  
Moving Object ◽  
Tracking Method ◽  
Kalman Filter Algorithm ◽  
Moving Object Tracking

An Improved Moving Object Tracking Method Based on Graph Cuts

2014 ◽  
Vol 596 ◽  
pp. 398-401
Author(s):  
Ming Jie Zhang ◽  
Bao Sheng Kang
Keyword(s):  
Object Tracking ◽  
Surveillance System ◽  
Traditional Method ◽  
Graph Cuts ◽  
Moving Object ◽  
Video Sequences ◽  
Tracking Method ◽  
Centroid Distance ◽  
Moving Object Tracking ◽  
Better Than

In order to improve efficiency of object tracking in occlusion states. A method to detect and automatically track was present in a surveillance system. Firstly, a graph cuts method was employed to segment image from a static scene. To identify foreground objects by positions and sizes of the obtained foreground regions. In addition, the performance to track objects was improved by using the improved overlap tracking method, the tracking method was used to analyze the centroid distance between neighboring objects and help object tracking in occlusion states of merging and splitting. By the experiments of moving object tracking in three video sequences, the experimental results exhibit that the proposed method is better than the traditional method.

Probabilistic Kalman filter for moving object tracking

2020 ◽  
Vol 82 ◽  
pp. 115751 ◽  
Author(s):  
Fahime Farahi ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Object ◽  
Moving Object Tracking

Moving object tracking with related multi-regions based on Kalman filter

2013 ◽  
Vol 32 (11) ◽  
pp. 3174-3177
Author(s):  
Wei WANG ◽  
Yu GUO ◽  
Xin YU
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Object ◽  
Moving Object Tracking

Video Tracking of Human with Occlusion Based on MeanShift and Kalman Filter

2013 ◽  
Vol 380-384 ◽  
pp. 3672-3677 ◽  
Author(s):  
Bao Hong Yuan ◽  
De Xiang Zhang ◽  
Kui Fu ◽  
Ling Jun Zhang
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Video Tracking ◽  
Moving Object ◽  
Large Area ◽  
Image Region ◽  
Future State ◽  
Kalman Filter Algorithm ◽  
Histogram Similarity

In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kalman filter algorithm. MeanShift object tracking algorithm uses a histogram to describe the color characteristics of an object, and search the location of an image region that the color histogram is closest to the histogram of the object. Histogram similarity is defined in terms of the Bhattacharya coefficient. When the moving object is a large area blocked, the future state of moving object is estimated by Kalman filter. Experimental results verify that the proposed algorithm achieves efficient tracking of moving objects under the confusing situations.

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