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关联动态特征的目标自适应跟踪算法
2022年电子技术应用第11期
孙志成1,董一杰2,胡爱兰2,张瑞权2
1.63861部队,吉林 白城137000;2.华北计算机系统工程研究所,北京100083
摘要: 在复杂的靶场试验场景中,试验现场常常涉及扬尘、强光、遮挡等多变的自然环境。针对这种情况下快速运动的目标物体跟踪,提出了一种关联动态特征的单目标跟踪算法。首先使用门控循环单元(Gated Recurrent Unit,GRU)提取待跟踪目标的时序动态特征,获得候选处理目标框集合;然后利用卷积网络(Convolutional Neural Network,CNN)提取候选目标框的深度卷积特征并确定目标位置,同时分离出背景卷积特征;在跟踪过程中,使用分离出的背景卷积特征图对网络进行参数更新,增强网络的鲁棒性与自适应性。实验结果表明,所提出的算法可以对靶场图像采集系统中的被试移动目标进行自适应跟踪,并且在复杂环境背景下算法仍能保持优异的鲁棒性与适应性。
中圖分類號(hào): TP18
文獻(xiàn)標(biāo)識(shí)碼: A
DOI:10.16157/j.issn.0258-7998.212358
中文引用格式: 孫志成,董一杰,胡愛(ài)蘭,等. 關(guān)聯(lián)動(dòng)態(tài)特征的目標(biāo)自適應(yīng)跟蹤算法[J].電子技術(shù)應(yīng)用,2022,48(11):57-62.
英文引用格式: Sun Zhicheng,Dong Yijie,Hu Ailan,et al. Adaptive tracking algorithm for target based on associated dynamic features[J]. Application of Electronic Technique,2022,48(11):57-62.
Adaptive tracking algorithm for target based on associated dynamic features
Sun Zhicheng1,Dong Yijie2,Hu Ailan2,Zhang Ruiquan2
1.63861 Troop,Baicheng 137000,China; 2.National Computer System Engineering Research Institute of China,Beijing 100083,China
Abstract: In the complex scene of shooting range test, the test site often involves the changeable natural environment including dust, strong light, occlusion, etc. A single target tracking algorithm associated with dynamic features is proposed to track fast moving targets in this case. Firstly, the gated recurrent unit is used to extract the time series dynamic characteristics of the target which need to be tracked, so as to obtain a set of candidate processing target frames. Then,convolutional network is adopted to extract the depth convolution features of the candidate target frame and determine target position, as well as separating the background convolution features. In the tracking process, the separated background convolution feature map is applied to update network parameters to enhance the robustness and adaptability of network. Experimental results show that the proposed algorithm can adaptively track moving target in the shooting range image acquisition system, which can still maintain excellent robustness and adaptability in the context of complex environment.
Key words : range test;adaptive tracking;gated recurrent unit;convolutional neural network

0 引言

    某型號(hào)系統(tǒng)在進(jìn)行靶場(chǎng)試驗(yàn)時(shí),需準(zhǔn)確定位并跟蹤被試設(shè)備,確保其能處于相應(yīng)試驗(yàn)系統(tǒng)范圍中,這對(duì)單目標(biāo)跟蹤提出了更高的要求。單目標(biāo)跟蹤逐漸成為計(jì)算機(jī)視覺(jué)所需研究和應(yīng)用的重點(diǎn)之一[1],為了滿足某些復(fù)雜場(chǎng)景的使用需求,對(duì)視頻中特定目標(biāo)進(jìn)行自適應(yīng)處理逐漸成為重要的需求。隨著近年來(lái)計(jì)算機(jī)技術(shù)的發(fā)展與算力的進(jìn)步,單目標(biāo)跟蹤被廣泛地應(yīng)用于軍事設(shè)施設(shè)備、安防監(jiān)控、無(wú)人駕駛等領(lǐng)域[2-4]

    國(guó)內(nèi)外相關(guān)學(xué)者根據(jù)不同的工作原理對(duì)跟蹤算法做了大量研究工作。Henriques[5]等提出了核相關(guān)濾波算法,但該算法在遮擋等因素影響下會(huì)出現(xiàn)跟蹤丟失的情況;Zhou[6]等提出了結(jié)合目標(biāo)位置、形狀、外觀的多核相關(guān)濾波算法,對(duì)實(shí)際海洋雷達(dá)目標(biāo)進(jìn)行跟蹤;盧楊[7]等通過(guò)改進(jìn)紋理特征并應(yīng)用于紅外目標(biāo)跟蹤,驗(yàn)證了其魯棒性與實(shí)時(shí)性;仇祝令[8]等考慮目標(biāo)的空時(shí)域特性對(duì)正則化項(xiàng)進(jìn)行約束求解,該方法在一定程度上提升了跟蹤的實(shí)時(shí)性與精確度。




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作者信息:

孫志成1,董一杰2,胡愛(ài)蘭2,張瑞權(quán)2

(1.63861部隊(duì),吉林 白城137000;2.華北計(jì)算機(jī)系統(tǒng)工程研究所,北京100083)




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