局部感知下的稀疏優(yōu)化目標(biāo)跟蹤方法

劉大千; 劉萬軍; 費(fèi)博雯

doi:10.11999/JEIT170473

局部感知下的稀疏優(yōu)化目標(biāo)跟蹤方法

doi: 10.11999/JEIT170473

1.
(遼寧工程技術(shù)大學(xué)電子與信息工程學(xué)院葫蘆島 125105)
2.
(遼寧工程技術(shù)大學(xué)軟件學(xué)院葫蘆島 125105)
3.
(遼寧工程技術(shù)大學(xué)工商管理學(xué)院葫蘆島 125105)

基金項(xiàng)目:

國家自然科學(xué)基金(61172144)，遼寧省科技攻關(guān)計(jì)劃項(xiàng)目(2012216026)

計(jì)量
- 文章訪問數(shù): 1461
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-05-17
- 修回日期: 2017-08-01
- 刊出日期: 2018-02-19

Object Tracking Method Based on Sparse Optimization of Local Sensing

1.
(School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China)
2.
(School of Software, Liaoning Technical University, Huludao 125105,China)
3.
(School of Business and Management, Liaoning Technical University, Huludao 125105, China)

Funds:

The National Natural Science Foundation of China (61172144), The Science and Technology Foundation of Liaoning Province (2012216026)

摘要

摘要: 針對傳統(tǒng)稀疏表示跟蹤算法在復(fù)雜背景中易出現(xiàn)跟蹤漂移問題，該文提出一種局部感知下的稀疏優(yōu)化目標(biāo)跟蹤方法。首先，將首幀確定的目標(biāo)區(qū)域進(jìn)行非重疊均勻分割，并利用目標(biāo)的全局特征和局部特征聯(lián)合建模。然后，提出一種局部感知校驗(yàn)方法約束稀疏優(yōu)化匹配過程，從而確定最優(yōu)匹配樣本。最后，在模板更新中提出一種決策方法對遮擋進(jìn)行檢測，并針對不同遮擋情況采取相應(yīng)的更新策略，使得更新后的模板集更加完善。實(shí)驗(yàn)在10個(gè)標(biāo)準(zhǔn)庫視頻序列中測試，并與目前較流行的目標(biāo)跟蹤算法在跟蹤效果、成功率等方面進(jìn)行比較，實(shí)驗(yàn)結(jié)果表明，提出的跟蹤方法在局部遮擋、目標(biāo)形變、復(fù)雜背景等條件下跟蹤準(zhǔn)確、適應(yīng)性強(qiáng)。
- 目標(biāo)跟蹤 /
- 局部感知 /
- 稀疏優(yōu)化 /
- 遮擋決策
Abstract: The problem of tracking drift is produced easily by traditional sparse representation tracking methods in complex scene. To solve this problem, a novel tracking approach based on sparse optimization of local sensing is proposed. Firstly, the object area of the first frame is divided into non-overlapping uniform segmentation, and building the template set using global features and local features. Then, a local sensing correction method for constraining sparse optimization matching process is utilized to determine the optimal matching samples. Finally, a new method of occlusion decision is used to detect occlusion, and updating strategies are adopted according to different occlusion conditions, which makes the template sets more complete in the process of template update. The experiments compare with state-of-the-art tracking algorithms on 10 tracking test sequences of benchmark library. Experiment results indicate that the proposed method possesses characteristics of accurate tracking and strong adaptability in the conditions of partial occlusion, deformation, and complex background.
- Object tracking /
- Local sensing /
- Sparse optimization /
- Occlusion decision

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