A novel visual tracking algorithm based on random sampling and color gradient

2011 ◽  
Author(s):  
Lifang Li ◽  
Yang Lu ◽  
Xianliang Tong ◽  
Man Liu
Keyword(s):  
Visual Tracking ◽  
Random Sampling ◽  
Tracking Algorithm ◽  
Color Gradient

Visual tracking algorithm based on the sparse-regularized subspace

2018 ◽  
Vol 26 (4) ◽  
pp. 989-997
Author(s):  
陈典兵 CHEN Dian-bing ◽  
朱明 ZHU Ming ◽  
王慧利 WANG Hui-li ◽  
杨航 YANG Hang
Keyword(s):  
Visual Tracking ◽  
Tracking Algorithm

scholarly journals Part-Based Visual Tracking via Online Weighted P-N Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Heng Fan ◽  
Jinhai Xiang ◽  
Jun Xu ◽  
Honghong Liao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Tracking Algorithm ◽  
Local Feature ◽  
Experimental Results ◽  
Learning Method ◽  
Target Model ◽  
Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

Visual tracking algorithm based on improved Bhattacharyya coefficient and model update strategy

2015 ◽  
Vol 36 (1) ◽  
pp. 52-57
Author(s):  
Huang An-qi ◽  
◽  
Hou Zhi-qiang ◽  
Yu Wang-sheng ◽  
Liu Xiang
Keyword(s):  
Visual Tracking ◽  
Tracking Algorithm ◽  
Bhattacharyya Coefficient ◽  
Model Update ◽  
Update Strategy

Target Recognition Application of Real-Time Optical Information Processing System

2014 ◽  
Vol 536-537 ◽  
pp. 197-200
Author(s):  
Lan Zhao ◽  
Tao Zeng
Keyword(s):  
Information Processing ◽  
Visual Tracking ◽  
Target Recognition ◽  
Tracking System ◽  
Processing System ◽  
Tracking Algorithm ◽  
Optical Information ◽  
Illumination Changes ◽  
Imaging Surveillance ◽  
Template Tracking

This paper focuses on the visual tracking algorithm in optical imaging surveillance and tracking system. The tracking particle filter framework deemed find sparse representation problem, can effectively overcome the visual tracking algorithm appears in noise, occlusion, background interference and complex situations such as illumination changes. Morphological methods using digital occlusion area is detected to determine whether the date is added to the template tracking results set, thereby updating the control template, to effectively prevent the drift tracking results.

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