Object Tracking Using Maximum Colour Distance under Illumination Change

Author:

Whoang Inteck1,Kim Jeong Heon2,Choi Kwang Nam2

Affiliation:

1. The Attached Institute of ETRI, Daejeon, Korea

2. Department of Computer Science and Engineering, Chung-Ang University, Seoul, Korea

Abstract

This paper presents a new method for tracking moving objects that have colour variations due to illumination, which uses a maximum colour distance on the mean shift framework. Conventional colour-based mean shift methods show good results when tracking non-rigid moving objects. However, they do not provide accurate results when the initial colour distribution of the object disappears. Our method uses the maximum colour distance to represent the objects. If a colour distance can be defined as the geometric distance between two colour points in a colour space, then the maximum colour distance is defined as the maximum value among all of the colour distances. The physical illumination model, under assumptions, guarantees that the maximum colour distance is independent of ambient lighting and other illumination with an identical solid angle. The objective of our method is to provide robust real-time object tracking with large colour variation in objects whose colour changes due to external illumination. The implementation of this new algorithm shows effective tracking results with a complete object colour change over time. The validation of our approach is illustrated by the comparison of experimental results obtained using the methods described above.

Publisher

SAGE Publications

Subject

Artificial Intelligence,Computer Science Applications,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking;2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2021-06

2. Object tracking algorithm for unmanned surface vehicle based on improved mean-shift method;International Journal of Advanced Robotic Systems;2020-05-01

3. Transportation Object Detection with Bag of Visual Words Model by PLSA and MLP;Mobile Networks and Applications;2018-07-11

4. Real-Time Head Pose Estimation Framework for Mobile Devices;Mobile Networks and Applications;2016-12-30

5. Effective pedestrian detection using deformable part model based on human model;International Journal of Control, Automation and Systems;2016-10-25

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