Frequency decomposition-based tracking for improving a CSK tracker

Object tracking is an important task within the field of computer vision. Object tracking methodology is divided into three categories: point-, kernel-; and silhouette-based tracking. Recently, a correlation-based kernel tracking has been used in object tracking with high accuracy and performance. H...

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Bibliographic Details
Published in2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) pp. 1 - 4
Main Authors DongWook Ju, Guisik Kim, Soowoong Jeong, Sangkeun Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Conferences
Correlation
Decomposition
Frequency-domain analysis
Kernel
Kernel correlation
Lighting
Object tracking
Target tracking
Visual tracking
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Summary:Object tracking is an important task within the field of computer vision. Object tracking methodology is divided into three categories: point-, kernel-; and silhouette-based tracking. Recently, a correlation-based kernel tracking has been used in object tracking with high accuracy and performance. However, the correlation-based approach heavily relies on luminance information, thus it has many problems. In addition, various environments including illumination variation, deformation, occlusion and scale variation make the object tracking unreliable especially in chronic problem such as state update. To isolate this problem, we present a frequency decomposition-based effective approach for more robust circulant structure kernel (CSK) traker. Experimental results show that the proposed approach outperforms the conventional CSK tracker by about 5% on average for 38 illumination changing sequences from the commonly used 100 test sequences.
DOI:10.1109/ICCE-Asia.2016.7804820