目的 动态场景图像中所存在的静态目标、背景纹理等静态噪声，以及背景运动、相机抖动等动态噪声，极易导致运动目标检测误检或漏检。针对这一问题，本文提出了一种基于运动显著性概率图的目标检测方法。方法 该方法首先在时间尺度上构建包含短期运动信息和长期运动信息的构建时间序列组；然后利用TFT（temporal Fourier transform）方法计算显著性值。基于此，得到条件运动显著性概率图。接着在全概率公式指导下得到运动显著性概率图，确定前景候选像素，突出运动目标的显著性，而对背景的显著性进行抑制；最后以此为基础，对像素的空间信息进行建模，进而检测运动目标。结果 对提出的方法在3种典型的动态场景中与9种运动目标检测方法进行了性能评价。3种典型的动态场景包括静态噪声场景、动态噪声场景及动静态噪声场景。实验结果表明，在静态噪声场景中，F_score提高到92.91%，准确率提高到96.47%，假正率低至0.02%。在动态噪声场景中，F_score提高至95.52%，准确率提高到95.15%，假正率低至0.002%。而在这两种场景中，召回率指标没有取得最好的性能的原因是，本文所提方法在较好的包络目标区域的同时，在部分情况下易将部分目标区域误判为背景区域的，尤其当目标区域较小时，这种误判的比率更为明显。但是，误判的比率一直维持在较低的水平，且召回率的指标也保持在较高的值，完全能够满足于实际应用的需要，不能抵消整体性能的显著提高。另外，在动静态噪声场景中，4种指标均取得了最优的性能。因此，本文方法能有效地消除静态目标干扰，抑制背景运动和相机抖动等动态噪声，准确地检测出视频序列中的运动目标。结论 本文方法可以更好地抑制静态背景噪声和由背景变化（水波荡漾、相机抖动等）引起的动态噪声，在复杂的噪声背景下准确地检测出运动目标，提高了运动目标检测的鲁棒性和普适性。

Objective Moving target detection is an important research content of image analysis technology. The purpose of it is to remove the background interference through a series of operations to extract and detect moving targets. It can be applied to video surveillance, image retrieval and motion analysis etc. The classical methods of moving object detection are mainly realized by extracting motion information of inter frames, detecting optical flow changes, or background modeling. But in dynamic scenarios, such as the scenarios which are affected by static noise (background is similar to moving target) and dynamic noise (noise which is caused by the branches, ripple and camera jitter), accuracy and robustness of moving target detection is greatly reduced. For this reason, many improved methods have been put forward. Some achievements have been made in static background noise scene or dynamic background noise scene. However, it is a pity that few methods achieve perfect results in both two situations. Aiming to solve the problems of false detection caused by static noise(background is similar to moving target) and dynamic noise (background changes or camera shaking etc.), this paper proposes a moving object detection method by using the motion saliency probability map and compares it with 9 moving object detection methods in 3 typical dynamic scenes. Method The motion saliency probability map enhances the saliency of the moving targets at the current frame by using the motion information in a long time and weakens the saliency of background and moving targets in historical frames. Therefore, the maximum of the probability value corresponds to the motion saliency of the current frame. The dynamic noise in the image (such as background branches, ripple and camera jitter) often causes a large number of mistakes in detection results. These pixels are not only gathered together, but also partially salient in movement, and can not be eliminated by morphological or noise filtering methods. However, the correlation between adjacent pixels can provide a high level of detection accuracy in dynamic background scenes, and can effectively suppress dynamic noise. In this method, the paper firstly constructs a set of time series including short-term motion information and long-term motion information on a time scale. Then the saliency value is calculated by the TFT method. Based on this, the conditional motion saliency probability map is obtained. Next, the motion saliency probability map is given under the guidance of the full probability formula, which can eliminate static noise, small dynamic noise and the influence of historical frame on moving targets. By segmenting the motion saliency probability map, the saliency of the moving target is highlighted, and the saliency of the background is suppressed. Finally, the spatial information of the pixel is modeled based on the motion saliency probability map to optimize the result, which can eliminate significant dynamic noise. The process of modeling is divided into two steps, including the calculation of shift probability map and component shift probability map. Foreground target can be extracted by binaryzating the component shift probability map. Generally speaking, the main innovation of this paper lies in:a saliency probability model is proposed. The paper combines the saliency value with the probability of occurrence of the moving targets. Besides, it fuses saliency detection model and probability model to construct a saliency probability model creatively. Result In this paper, the proposed method is compared with 9 moving object detection methods in 3 typical dynamic scenes, including static background noise scene, dynamic background noise scene caused by camera shake and dynamic background noise scene caused by water ripple. The experimental results show that in the static noise scene, F_score is increased to 92.91%, precision rate is increased to 96.47% and false positive rate is as low as 0.02%. In the dynamic noise scene caused by camera shake, F_score is in creased to 95.52%, precision is increased to 95.15%, and false positive rate is as low as 0.002%. In these two scenarios, the index recall can not achieve the best performance, because in some cases, the method proposed in this paper is easy to misjudge some of the target areas as the background area while it can better envelope target region, especially when the target region is small, and in this case, ratio of this misjudgment is more obvious. However, false positive rate has been maintained at a low level, and recall rate is kept at a higher value, which can fully meet the needs of practical applications. So while recall rate doesn't perform best, this can not offset the significant improvement in overall performance. In addition, in the dynamic background noise scene caused by water ripple, the four indexes all achieve the best performance. In general, the proposed method can eliminate static object interference and suppress dynamic background noise, accurately detecting the moving object in video sequence. Conclusion To solve the problems of false detection caused by static noise and dynamic noise, a novel target detection method is proposed in this paper. In general, the method can be divided into three parts. Firstly, the paper construct a set of time series. Then the saliency value is calculated and we can get the conditional motion saliency probability map and the saliency probability map respectively. Finally, the spatial information of the pixel is modeled, including calculating the shift probability map and component shift probability map, and the moving object is detected. In the paper, nine methods (GMM,KDE, ViBe,PQFT, DiscSal, ROSL,RMAMR, ManhNMF and Deep-Semi-NMF) are compared with the proposed method in 3 typical dynamic scenes. Experiments show that the proposed method can better suppress the static background noise and the dynamic noise caused by background change (such as water ripple, camera shake, etc.), and accurately detect moving objects.