基于感知深度神經(jīng)網(wǎng)絡(luò)的視覺(jué)跟蹤

侯志強(qiáng); 戴鉑; 胡丹; 余旺盛; 陳晨; 范舜奕

doi:10.11999/JEIT151449

基于感知深度神經(jīng)網(wǎng)絡(luò)的視覺(jué)跟蹤

doi: 10.11999/JEIT151449

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61175029, 61473309)，陜西省自然科學(xué)基金(2015JM6269，2015JM6269，2016JM6050)

計(jì)量
- 文章訪問(wèn)數(shù): 1829
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-12-22
- 修回日期: 2016-05-04
- 刊出日期: 2016-07-19

Robust Visual Tracking via Perceptive Deep Neural Network

Funds:

The National Natural Science Foundation of China (61175029, 61473309), The Natural Science Foundation of Shaanxi Province (2015JM6269, 2015JM6269, 2016JM6050)

摘要

摘要: 視覺(jué)跟蹤系統(tǒng)中，高效的特征表達(dá)是決定跟蹤魯棒性的關(guān)鍵，而多線索融合是解決復(fù)雜跟蹤問(wèn)題的有效手段。該文首先提出一種基于多網(wǎng)絡(luò)并行、自適應(yīng)觸發(fā)的感知深度神經(jīng)網(wǎng)絡(luò)；然后，建立一個(gè)基于深度學(xué)習(xí)的、多線索融合的分塊目標(biāo)模型。目標(biāo)分塊的實(shí)現(xiàn)成倍地減少了網(wǎng)絡(luò)輸入的維度，從而大幅降低了網(wǎng)絡(luò)訓(xùn)練時(shí)的計(jì)算復(fù)雜度；在跟蹤過(guò)程中，模型能夠根據(jù)各子塊的置信度動(dòng)態(tài)調(diào)整權(quán)重，提高對(duì)目標(biāo)姿態(tài)變化、光照變化、遮擋等復(fù)雜情況的適應(yīng)性。在大量的測(cè)試數(shù)據(jù)上進(jìn)行了實(shí)驗(yàn)，通過(guò)對(duì)跟蹤結(jié)果進(jìn)行定性和定量分析表明，所提出算法具有很強(qiáng)的魯棒性，能夠比較穩(wěn)定地跟蹤目標(biāo)。
- 視覺(jué)跟蹤 /
- 特征表達(dá) /
- 深度學(xué)習(xí) /
- 感知深度神經(jīng)網(wǎng)絡(luò)
Abstract: In a visual tracking system, the feature description plays the most important role. Multi-cue fusion is an effective way to solve the tracking problem under many complex conditions. Therefore, a perceptive deep neural network based on multi parallel networks which can be triggered adaptively is proposed. Then, using the multi-cue fusion, a new tracking method based on deep learning is established, in which the target can be adaptively fragmented. The fragment decreases the input dimension, thus reducing the computation complexity. During the tracking process, the model can dynamically adjust the weights of fragments according to the reliability of them, which is able to improve the flexibility of the tracker to deal with some complex circumstances, such as target posture change, light change and occluded by other objects. Qualitative and quantitative analysis on challenging benchmark video sequences show that the proposed tracking method is robust and can track the moving target robustly.
- Visual tracking /
- Feature description /
- Deep learning /
- Perceptive deep neural network

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