Gong Jiulu, Fan Guoliang, Yu Liangjiang, Havlicek Joseph P, Chen Derong, Fan Ningjun
School of Mechatronical Engineering, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China.
School of Electrical and Computer Engineering, Oklahoma State University, 202 Engineering South, Stillwater, OK 74078, USA.
Sensors (Basel). 2014 Jun 10;14(6):10124-45. doi: 10.3390/s140610124.
We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure in the latent space that is embedded with one view-independent identity manifold and infinite identity-dependent view manifolds. In the ATR-Seg algorithm, the ATR problem formulated as a sequential level-set optimization process over the latent space of JVIM, so that tracking and recognition can be jointly optimized via implicit shape matching where target segmentation is achieved as a by-product without any pre-processing or feature extraction. Experimental results on the recently released SENSIAC ATR database demonstrate the advantages and effectiveness of ATR-Seg over two recent ATR algorithms that involve explicit shape matching.
我们提出了一种用于红外图像的新的集成目标跟踪、识别和分割算法，称为ATR-Seg。ATR-Seg是在概率形状感知水平集框架中制定的，该框架结合了用于目标形状建模的联合视图-身份流形（JVIM）。作为一种形状生成模型，JVIM在潜在空间中具有统一的流形结构，该结构嵌入了一个与视图无关的身份流形和无限个与身份相关的视图流形。在ATR-Seg算法中，ATR问题被表述为在JVIM潜在空间上的顺序水平集优化过程，以便通过隐式形状匹配联合优化跟踪和识别，其中目标分割作为副产品实现，无需任何预处理或特征提取。在最近发布的SENSIAC ATR数据库上的实验结果证明了ATR-Seg相对于两种涉及显式形状匹配的最新ATR算法的优势和有效性。
