scholarly journals Adaptive Colour Feature Identification in Image for Object Tracking

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Feng Su ◽  
Gu Fang ◽  
Ngai Ming Kwok

Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

2013 ◽  
Vol 756-759 ◽  
pp. 4021-4025 ◽  
Author(s):  
Yi Zhi Zhao ◽  
Huan Wang ◽  
Guo Cai Yin

Computer vision is a diverse and relatively new field of study. Object tracking plays a crucial role as a preliminary step for high-level image processing in the field of computer vision. However, mean shift algorithm in the target tracking has some defects, such as: the application of fixed bandwidth for probability density estimation usually causes lack of smooth or too smooth; moving target often appears partial occlusion or complete occlusion due to the complexity of the background; background pixels in object model will induce localization error of object tracking, and so on. Therefore, this paper elaborates several elegant algorithms to solve some of the problems. After discussing the application of Mean shift in the field of target tracking, this paper presented an improved Mean shift algorithm by combining Mean Shift and Kalman Filter.


2021 ◽  
Vol 438 ◽  
pp. 94-106
Author(s):  
Shiyu Xuan ◽  
Shengyang Li ◽  
Zifei Zhao ◽  
Zhuang Zhou ◽  
Wanfeng Zhang ◽  
...  

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