实验结果证明了本文采用的运动目标检测与跟踪算法的有效性，此外该算法具有良好的通用性，用于可见光图像序列也能收到好的效果。
Experimental results show that the proposed moving objects detection and tracking algorithm works well in not only infrared image sequences but also visual ones.
仿真实验结果表明，该算法具有较高的检测率和良好的实时特性，能有效地检测出低信比红外图像序列中的弱小运动目标。
Simulations of real IR image sequence with low SNR prove the algorithm can effectively detect dim moving target, and has high detection probability and excellent real time performance.
通过实验结果可以看出，该算法不仅能适应复杂多样的背景环境，而且对运动目标的连续检测能力具有位移不变、伸缩不变和旋转不变的特性。
Experimental results show that the algorithm has an invariant property with respect to the shift, scale and rotation of the moving target in the continuing detection of moving targets.
不同差分方法下的实验结果表明，这种基于差分图象的运动目标检测框架是行之有效的。
A great deal of experiments in the case of different difference show that this framework is effective.
在对点目标检测识别算法仿真实验分析的基础上，开发了实时运动点目标检测处理的软件系统。
According to the experimenting and analyzing of the detecting algorithm of the point targets, a real-time detecting software of the moving point targets is developed.
实验结果表明，该算法对实时检测在复杂背景的红外图像中运动的弱小目标具有很好的效果。
The experiment results showed that this method is effective for detecting the small infrared target under a complex scene in real time.
实验结果表明在静止背景的图像序列中，该方法比传统方法能更好地检测出以不同速度运动的目标。
The results show that the algorithm can accurately and validly detect different objects with different velocities in static background.
实验结果表明，该算法正确检测运动对象的精度高，能够检测出慢速物体和小目标的运动，并较好地解决了遮挡问题。
The experimental results show that the algorithm can detect moving objects precisely, including slow or little objects, and also solve the occlusions preferably.
实验结果表明该方法可以在复杂的背景中检测多个海上运动目标，具有较好的鲁棒性。
The experiment results show that the algorithm is robust in detecting multi-objects in complicated background.
实验结果表明，该方法与传统高斯混合背景模型相比，有较好的学习能力与稳定性，能提高运动目标检测的正确率。
Experimental results show that compared with moving object detection approach based on conventional Gaussian mixture model, it has a desirable stability and learning ability.
实验表明，该算法可对多类多个运动目标进行有效检测，且具有较高的实时性与鲁棒性。
Experimental results show that the algorithm can be used for kinds of objects detection effectively, with high real-time and robustness performance.
实验结果表明，该算法能对视频中的运动目标实现检测和连续跟踪，对遮挡也有较好的鲁棒性。
The experimental results demonstrate that the proposed algorithm can detect and track the moving object consecutively in video and has better robustness to occlusion.
实验结果表明，本算法相对于均值偏移算法有较好的改进，能对视频中的运动目标实现检测和连续跟踪，对遮挡也有较好的鲁棒性。
The results demonstrated that the algorithm has great improvement relative to mean shift, it could detect and track the video moving target continuously and had preferable robustness for occlusion.
仿真实验的结果验证了运动背景补偿算法的有效性，以及运动背景下基于运动补偿的目标检测的可行性。
The experiments results proved the validity of background motion compensation algorithm and the feasibility of object-detecting based on the background motion compensation.
通过调整运动模型，可以应用于不同运动特征的目标检测问题。实验结果证明了本文方法的有效性和鲁棒性。
By modify motion model, targets with different motion features could be detected. Experiments show that the proposed method is feasible and robust.
实验结果表明，该算法能有效地检测出运动小目标的位置。
The experiment results show that the algorithm can detect the true moving small target effectively.
实验结果表明，该算法能有效地检测出投射阴影和运动目标，具有较高的实际应用价值。
Experimental results demonstrate the proposed algorithm can effectively detect cast shadow and moving object, and has higher practicability.
在运动目标检测研究方面，首先介绍了静态背景下运动目标检测的三种算法，通过实验分析了三种算法的优缺点。
On the research of the motion detection, firstly three main algorithms of motion detection under a static background and its analyzed advantages and drawbacks of these algorithms are researched.
在运动目标检测研究方面，首先介绍了静态背景下运动目标检测的三种算法，通过实验分析了三种算法的优缺点。
On the research of the motion detection, firstly three main algorithms of motion detection under a static background and its analyzed advantages and drawbacks of these algorithms are researched.
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