|Table of Contents|

TLD Object Tracking Algorithm Based on Kernelized Correlation Filter(PDF)

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

Issue:
2020年04期
Page:
37-43
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
TLD Object Tracking Algorithm Based on Kernelized Correlation Filter
Author(s):
Dong Chunyan; Liu Huai; Liang Qinjia
School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China
Keywords:
object tracking; kernelized correlation filter; feature fusion; occlusion detection
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2020.04.006
Abstract:
TLD tracker is improved in this paper in order to increase the tracking performance when the tracked object scale is changing or in occlusion. For the tracker,KCF tracker is used to replace the original tracker of TLD algorithm,and KCF tracker is improved as follows:the Lab color feature of the object is added in feature extraction,scale estimation is introduced in search for the object location,in the model updating stage,and tracking state judgment mechanism is adopted to determine the reliability of the tracking results by setting the threshold value of maximum output response,the threshold value of APCE and the threshold value of the random fern classifier,so as to improve the tracking effect of the tracker under the conditions of scale variation,occlusion and illumination variation. For the detector,in order to reduce a large number of meaningless windows before detection,Kalman filter is used to estimate the object position,and then an improved cascade classifier is used to locate the object more accurately around the estimated position. Variance classifier and random fern classifier are still used in the first two levels of the improved cascade classifier,and the third level is classified by the improved KCF tracker. 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 that it can ensure the real-time performance.

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Last Update: 2020-12-15