Adaptive Kernel Correlation Filter Tracking Algorithm in Complex Scenes

Jin Sun

doi:10.1109/ACCESS.2020.3038792

Adaptive Kernel Correlation Filter Tracking Algorithm in Complex Scenes

2020-01-01

8

OA

AI

DOI：10.1109/ACCESS.2020.3038792

原文链接

查看原文

摘要

En 中文

The traditional kernel correlation filter (KCF) algorithm has poor tracking results in complex scenes with severe occlusion, deformation, and low resolution and cannot achieve long-term tracking. To improve the accuracy of the tracking algorithm in complex scenes, an adaptive kernel correlation filter algorithm is proposed. First, a multifeature complementary scheme is proposed that linearly weights the responses of the histogram of oriented gradient (HOG) features and color features and learns a target position estimation model to realize target position estimation. Then, an adaptive scale model for estimating the scale transformation of the target is learned by extracting the HOG features of the object. Finally, according to occlusion judgment criteria, the Kalman filter is introduced to correct the position of the tracking target. The accuracy and success rate of the proposed algorithm are verified by simulation analysis on TC-128/OTB2015 benchmarks. Extensive experimental results illustrate that the proposed tracker achieves competitive performance compared with state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.899 and 0.635, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes. This study provides references for computer vision processing, such as image retrieval, behavior analysis, and intelligent driving.

Keyword:

Target tracking

Feature extraction

Correlation

Kernel

Adaptation models

Histograms

Sun

Object tracking

histogram of oriented gradient

Kalman filter

kernel correlation filter

Adaptive Kernel Correlation Filter Tracking Algorithm in Complex Scenes

摘要

AI总结

期刊

机构