|Table of Contents|

Kernelized Correlation Filter Tracking AlgorithmBased on Adaptive Feature Fusion(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2020年03期
Page:
50-56
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Kernelized Correlation Filter Tracking AlgorithmBased on Adaptive Feature Fusion
Author(s):
Dong Chunyan; Liu Huai; Liang Qinjia; Liang Lei
School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China
Keywords:
object tracking; kernelized correlation filter; feature fusion; model update
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2020.03.009
Abstract:
In the kernelized correlation filter object tracking algorithm,the use of mere histograms of oriented gradients(HOG)feature can easily cause insufficient feature expression. In addition,the linear interpolation model updating strategy used in this algorithm can easily cause model drift. To solve these problems,an improved kernelized correlation filter tracking algorithm based on adaptive feature fusion and model updating is proposed in this paper. Firstly,PCA is applied to reduce the dimension of HOG feature and color name(CN)feature to improve the speed of the algorithm. Secondly,the response maps of the two dimensionality reduction features are calculated independently. After that,the product of the peak value and the average peak-to-correlation energy(APCE)of two response maps are used to obtain their weights so that we can use weights to obtain the fused response map. Finally,the model updating rate is determined according to the similarity of CN feature between the two frames. Qualitative and quantitative analysis of experimental results on the OTB-50 dataset show that the tracking performance of the proposed algorithm is superior to that of other tracking algorithms and it can ensure the real-time performance.

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Last Update: 2020-09-15