A Saliency-Based Object Tracking Method for UAV Application

2018 ◽  
pp. 115-125
Author(s):  
Jinyu Yang ◽  
Wenrui Ding ◽  
Chunlei Liu ◽  
Zechen Ha
Keyword(s):  
Object Tracking ◽  
Tracking Method

Improved object tracking method based on mean shift and particle filter

2013 ◽  
Vol 32 (2) ◽  
pp. 504-506
Author(s):  
Ke LI ◽  
Ke-hu XU ◽  
Da-shan HUANG
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Mean Shift ◽  
Tracking Method

Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution

2021 ◽  
Author(s):  
Xuezhi Xiang ◽  
Wenkai Ren ◽  
Yujian Qiu ◽  
Kaixu Zhang ◽  
Ning Lv
Keyword(s):  
Object Tracking ◽  
Tracking Method

Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

2020 ◽  
pp. 2155002
Author(s):  
Xiuhua Hu ◽  
Yuan Chen ◽  
Yan Hui ◽  
Yingyu Liang ◽  
Guiping Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Scale Model ◽  
Correlation Filters ◽  
Motion Feature ◽  
Tracking Method ◽  
Object State ◽  
Multi Scale ◽  
Tracking Process ◽  
Object Appearance ◽  
Response Peak

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

A new real-time robust object tracking method

2015 ◽  
Author(s):  
Xiaofen Xing ◽  
Fuhao Qiu ◽  
Xiangmin Xu ◽  
Chunmei Qing
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Tracking Method

Novel object tracking method using the block-based motion estimation

2007 ◽  
Author(s):  
H eon Soo Shin ◽  
Sung Min Kim ◽  
Joo Woong Kim ◽  
Ki Hwan Eom
Keyword(s):  
Motion Estimation ◽  
Object Tracking ◽  
Novel Object ◽  
Tracking Method ◽  
Block Based
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