|Table of Contents|

Object Tracking Based on Transfer Learning Fusion of Dual HOG Feature(PDF)

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

Issue:
2022年04期
Page:
29-35
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Object Tracking Based on Transfer Learning Fusion of Dual HOG Feature
Author(s):
Huang Xuxia1; Lin Shubin2
(1.College of Information Technology and Engineering,Ningde Vocational and Technology College,Ningde 355000,China)
(2.College of Computer Science,Minnan Normal University,Zhangzhou 363000,China)
Keywords:
computer vision; object tracking; VGG network; HOG features; transfer learning
PACS:
TP391.4
DOI:
10.3969/j.issn.1672-1292.2022.04.004
Abstract:
Object tracking is one of the key technologies in computer vision, which is applied in image processing,pattern recognition,automatic control and other fields. Deep learning tracking algorithm has good performance. However,in the case of fast motion,the low-level HOG feature is easily affected and the tracking performance is weak. This paper proposes a robust tracking method based on offline training depth feature. Through offline training of VGG model,constructing double HOG features online and making optimal selection, the features extracted from offline training to online are transfered and integrated with the optimal HOG feature response. Firstly,the VGG network is trained layer by layer offline,and the convolution layer is responsible for extracting convolution features. Then,the HOG feature of the current frame in the object area is extracted and decomposed into HOG1 and HOG2. The optimal feature is selected for the two HOG features processed by filtering. Finally,the feature response graph is calculated by integrating convolution feature response and HOG feature response,that is,the predicted new object position. Comparing OTB-2013 and OTB-2015 benchmark data sets with other six algorithms,experimental results show that this method has good performance in dealing with fast motion,background clutter,deformation and other tracking aspects.

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Last Update: 2022-12-15