Adaptive Target Tracking based on Kernel SVM and HOG Features

Adaptive Target Tracking based on Kernel SVM and HOG Features

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2018 by IJRES Journal
Volume-5 Issue-1
Year of Publication : 2018
Authors : Yarui Wang, Zuolei Sun, Xiaoyu Wang
DOI :    10.14445/23497157/IJRES-V5I1P103

Citation 

MLA Style :Yarui Wang, Zuolei Sun, Xiaoyu Wang  "Adaptive Target Tracking based on Kernel SVM and HOG Features" International Journal of Recent Engineering Science 5.1(2018):11-15. 

APA Style :Yarui Wang, Zuolei Sun, Xiaoyu Wang. Adaptive Target Tracking based on Kernel SVM and HOG Features.  International Journal of Recent Engineering Science, 5(1),11-15.

Abstract
The traditional target tracking algorithm is popular with its real-time. Based on its discriminative model, this paper extracts HOG features into the Kernel SVM classifier to distinguish the foreground and background, which also uses a sliding window with an adaptive scale to divide the image into different scale image blocks. Meanwhile, aiming to improve the speed of calculation, we perform Fast Fourier Transform on the input image to process it in the frequency domain and then obtain rotating invariant gradient information as its feature map to distinguish the target and background the kernel SVM classifier. We performed our experiments on six challenging videos from the visual tracker benchmark and compared them with two well-known KCF and CT tracking algorithms. The experiment results show that our approach can track the targets accurately in a fast way, even though there are challenges such as motion blur and occlusion in the testing videos.

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Keywords
Object tracking, HOG feature, kernel SVM