scholarly journals Feature Selection Tracking Algorithm Based on Sparse Representation

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Hui-dong Lou ◽  
Wei-guang Li ◽  
Yue-en Hou ◽  
Qing-he Yao ◽  
Guo-qiang Ye ◽  
...  
Keyword(s):  
Feature Selection ◽  
Sparse Representation ◽  
Target Tracking ◽  
Visual Tracking ◽  
Fast Algorithm ◽  
The State ◽  
Tracking Algorithm ◽  
Complex Environment

In order to enhance the robustness of visual tracking algorithm in complex environment, a novel visual tracking algorithm based on multifeature selection and sparse representation is proposed. In the framework of particles filter, particles with low target similarity are first filtered out by a fast algorithm; then, based on the principle of sparsely reconstructing the sample label, the features with high differentiation against the background are involved in the computation so as to reduce the disturbance of occlusions and noises. Finally, candidate targets are linearly reconstructed via sparse representation and the sparse equation is solved by using APG method to obtain the state of the target. Four comparative experiments demonstrate that the proposed algorithm in this paper has effectively improved the robustness of the target tracking algorithm.

scholarly journals Unmanned Aerial Vehicle Video-Based Target Tracking Algorithm Using Sparse Representation

2019 ◽  
Vol 6 (6) ◽  
pp. 9689-9706 ◽  
Author(s):  
Minjie Wan ◽  
Guohua Gu ◽  
Weixian Qian ◽  
Kan Ren ◽  
Xavier Maldague ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Tracking ◽  
Unmanned Aerial Vehicle ◽  
Tracking Algorithm ◽  
Aerial Vehicle

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.

scholarly journals On-Line Visual Tracking with Occlusion Handling

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 929 ◽  
Author(s):  
Tharindu Rathnayake ◽  
Amirali Khodadadian Gostar ◽  
Reza Hoseinnezhad ◽  
Ruwan Tennakoon ◽  
Alireza Bab-Hadiashar
Keyword(s):  
Target Tracking ◽  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Occlusion Handling ◽  
Art Methods ◽  
Multi Target Tracking ◽  
Motion Features ◽  
On Line ◽  
Tracking Point

One of the core challenges in visual multi-target tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of déjà vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled Multi-Bernoulli (GLMB) filter, originally designed for tracking point-sized targets, to be used in visual multi-target tracking. The proposed algorithm includes a novel false alarm detection/removal and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the state-of-the-art methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the state-of-the-art methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.

The Target Tracking Algorithm Research of Independent Vision Robot Fish

2013 ◽  
Vol 753-755 ◽  
pp. 2015-2019
Author(s):  
Ming Zhang ◽  
Li Wang ◽  
Hai Hua Shi ◽  
Wei Xiang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Water Wave ◽  
Tracking Algorithm ◽  
Complex Environment ◽  
Single Target ◽  
Robot Fish ◽  
Single Target Tracking ◽  
Combined Algorithm

In the independent vision robot fish games, the interference of water wave often causes tracking inaccuracy and target tracking failure. In order to solve these problems, the Meanshift algorithm and the combination of Meanshift algorithm and Kalman filter respectively are studied to realize target tracking of independent vision robot fish in this paper. By comparing the two algorithms, the results show that: the former tracking algorithm is not ideal and easy to lose the target. The combined algorithm of Meanshift and Kalman filter can effectively improve the performance of single-target tracking in a complex environment to achieve the goal of continuous accurate tracking.

scholarly journals Robust visual tracking algorithm based on bidirectional sparse representation

2014 ◽  
Vol 63 (23) ◽  
pp. 234201
Author(s):  
Wang Bao-Xian ◽  
Zhao Bao-Jun ◽  
Tang Lin-Bo ◽  
Wang Shui-Gen ◽  
Wu Jing-Hui
Keyword(s):  
Sparse Representation ◽  
Visual Tracking ◽  
Tracking Algorithm
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