A moving target recognition method is proposed in this paper, which is based on multi-features fusion.
本文基于多特征融合，提出了一种运动目标识别方法。
The thesis is focused on the study of algorithm for moving target recognition and tracking in video sequence images.
本论文主要研究视频序列图像中运动目标识别与跟踪的算法。
A new recognition algorithm of small moving target based on multi-feature fusion is presented.
提出一种新的基于多特征融合的弱小运动目标识别方法。
Recognition algorithms for small moving target in strong noise based on single feature has a high false alarm. Sometimes the features of target and noise are very alike.
利用单个特征识别强噪声中的弱小运动目标，常因所提取的目标特征与噪声特征易混淆而导致高的虚警率。
A recognition and tracking algorithm adapted to the moving target is described.
讨论了一种对运动目标的目标识别跟踪算法。
A method of UWB radar target recognition based on moving correlation was put forward in terms of moving correlation theory combining with high resolution range imaging of UWB radar.
本文基于滑动相关理论，结合超宽带(uwb)雷达能获取目标高分辨力距离像的特性，提出了一种基于滑动相关的UWB雷达目标识别方法。
The detection and recognition of infrared moving small target in a sky background is considered in this paper.
研究了天空背景下红外运动小目标的检测与识别。
A gait recognition system consists of three primary parts: moving target detection, feature extraction and gait recognition.
它的研究主要由三部分构成：运动目标检测、特征提取和步态识别。
Recognition algorithms for small moving target in strong noise based on single feature has a high false alarm.
利用单个特征识别强噪声中的弱小运动目标，常因所提取的目标特征与噪声特征易混淆而导致高的虚警率。
Accordingly, the research, in the paper, provides a new technical means for the recognition and detection of underwater moving-target.
因此，本研究为水下运动目标识别与检测提供了新的技术途径。在工程实践中，具有重要的指导意义和应用价值。
Low SNR small moving target detection is a key technique in a target detection, recognition and tracking system.
在目标检测、识别与跟踪系统中，弱小目标的检测是需要解决的关键技术之一。
The simulation results using Moving and Stationary Target Acquisition and Recognition (MSTAR) data indicate that the error of the proposed method is small, and thus it has high accuracy.
移动与静止目标获取与识别（MSTAR）公共数据库实测数据的仿真结果表明，该方法估计误差较小，可获得较高的估计准确度。
The simulation results using Moving and Stationary Target Acquisition and Recognition (MSTAR) data indicate that the error of the proposed method is small, and thus it has high accuracy.
移动与静止目标获取与识别（MSTAR）公共数据库实测数据的仿真结果表明，该方法估计误差较小，可获得较高的估计准确度。
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