Occlusion Handling Based on Projective Depth in Object Tracking

2015 ◽  
Author(s):  
Hwa-Jin Park
Keyword(s):  
Object Tracking ◽  
Occlusion Handling

Enhanced occlusion handling and multipeak redetection for long-term object tracking

2018 ◽  
Vol 27 (03) ◽  
pp. 1
Author(s):  
Wei Zhang ◽  
Baosheng Kang ◽  
Shunli Zhang
Keyword(s):  
Object Tracking ◽  
Occlusion Handling

scholarly journals Occlusion Handling in Videos Object Tracking: A Survey

2014 ◽  
Vol 18 ◽  
pp. 012097 ◽  
Author(s):  
B Y Lee ◽  
L H Liew ◽  
W S Cheah ◽  
Y C Wang
Keyword(s):  
Object Tracking ◽  
Occlusion Handling

scholarly journals The Application of Image Processing to Solve Occlusion Issue in Object Tracking

2018 ◽  
Vol 152 ◽  
pp. 03001
Author(s):  
Yun Zhe Cheong ◽  
Wei Jen Chew
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Object Segmentation ◽  
Target Object ◽  
Occlusion Handling ◽  
Multiple Objects ◽  
The Road ◽  
Occluded Objects ◽  
On The Road ◽  
Tracking Object

Object tracking is a computer vision field that involves identifying and tracking either a single or multiple objects in an environment. This is extremely useful to help observe the movements of the target object like people in the street or cars on the road. However, a common issue with tracking an object in an environment with many moving objects is occlusion. Occlusion can cause the system to lose track of the object being tracked or after overlapping, the wrong object will be tracked instead. In this paper, a system that is able to correctly track occluded objects is proposed. This system includes algorithms such as foreground object segmentation, colour tracking, object specification and occlusion handling. An input video is input to the system and every single frame of the video is analysed. The foreground objects are segmented with object segmentation algorithm and tracked with the colour tracking algorithm. An ID is assigned to each tracked object. Results obtained shows that the proposed system is able to continuously track an object and maintain the correct identity even after is has been occluded by another object.

Context-Aware Three-Dimensional Mean-Shift With Occlusion Handling for Robust Object Tracking in RGB-D Videos

2019 ◽  
Vol 21 (3) ◽  
pp. 664-677 ◽  
Author(s):  
Ye Liu ◽  
Xiao-Yuan Jing ◽  
Jianhui Nie ◽  
Hao Gao ◽  
Jun Liu ◽  
...  
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Three Dimensional ◽  
Context Aware ◽  
Occlusion Handling
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