基于拉普拉斯分布模型的静止物体检测方法

针对监控视频中静止物体的检测,提出了一种基于拉普拉斯分布模型的检测方法。该方法首先改进?-D背景建模方法,快速提取视频背景,构成初级背景,然后在初级背景中引入拉普拉斯分布模型,从而构成精确的自适应动态背景,最后比较初级背景与动态背景之间的差异达到检测静止物体的目的。实验结果表明,该方法能在标准视频数据库中有效地检测到静止行李,并对人群拥挤和光照变化等复杂场景有良好的检测效果。

Review of Ultraviolet Non-Line-of-Sight Communication

With rapid advances of solar blind ultraviolet LED and ultraviolet detecting technology in recent years, ultraviolet communication gradually becomes a research hotspot due to its inherent advantages: low solar background noise, non-line-of-sight(NLOS) and good secrecy. The strong scattering characteristics in atmospheric render ultraviolet waveband the ideal choice for achieving NLOS optical communication. This paper reviews the research history and status of ultraviolet communication both in China and abroad, and especially introduces three main issues of ultraviolet communication: channel model, system analysis and design, light sources and detectors. For each aspect, current open issues and prospective research directions are analyzed.

Specific-Scene Oriented Pedestrian Detection in Visual Sensor Network

Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.

PERFORMANCE EVALUATION OF OSSO-CFAR WITH BINARY INTEGRATION IN WEIBULL BACKGROUND

The performance of the Ordered-Statistic Smallest Of (OSSO) Constant False Alarm Rate (CFAR) with binary integration in Weibull background with known shape parameter is analyzed, in the cases that the processor operates in homogeneous background and non-homogeneous situation caused by multiple targets and clutter edge. The analytical models of this scheme for the performance evaluation are given. It is shown that the OSSO-CFAR with binary integration can greatly improve the detection performance with respect to the single pulse processing case. As the clutter background becomes spiky, a high threshold S of binary integration (S/M) is required in order to obtain a good detection performance in homogeneous background. Moreover, the false alarm performance of the OSSO-CFAR with binary integration is more sensitive to the changes of shape parameter or power level of the clutter background.

Adaptive moving target detection algorithm based on Gaussian mixture model

In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.

A background refinement method based on local density for hyperspectral anomaly detection

For anomaly detection,anomalies existing in the background will affect the detection performance.Accordingly,a background refinement method based on the local density is proposed to remove the anomalies from thebackground.In this work,the local density is measured by its spectral neighbors through a certain radius which is obtained by calculating the mean median of the distance matrix.Further,a two-step segmentation strategy is designed.The first segmentation step divides the original background into two subsets,a large subset composed by background pixels and a small subset containing both background pixels and anomalies.The second segmentation step employing Otsu method with an aim to obtain a discrimination threshold is conducted on the small subset.Then the pixels whose local densities are lower than the threshold are removed.Finally,to validate the effectiveness of the proposed method,it combines Reed-Xiaoli detector and collaborative-representation-based detector to detect anomalies.Experiments are conducted on two real hyperspectral datasets.Results show that the proposed method achieves better detection performance.

Sensitivity of the suspected blood indicator:An experimental study

AIM： To investigate whether suspected blood indicator （SBI） in capsule endoscopy （CE） is affected by back- ground color and capsule passage velocity. METHODS： Experimental models of the small intestine constructed from paper in a variety of colors were used to simulate the background colors observed in CE im- ages. The background colors studied included very pale yellow, yellow, very pale magenta, light grayish pink, burnt sienna, and deep and dark brown, and red spots were attached inside them. An endoscopic capsule was manually passed through the models. The rate of detection of the red spots by the SBI was evaluated based on the colors of the models and the capsule pas- sage velocities （0.5 cm/s, 1 cm/s, and 2 cm/s）.RESULTS： The rate of detection of the red spots byground color of the model （P 〈 0.001）. Detection rates were highest for backgrounds of very pale magenta, burnt sienna, and yellow, in that order. They were lowest for backgrounds of dark brown and very pale yellow. The rate of detection of red spots by the SBI tended to decrease at rapid capsule passage velocities （1-2 cm/s） compared to slow velocities （0.5 cm/s） for backgrounds of very pale yellow （P = 0.042）, yellow （P = 0.001）, very pale magenta （P = 0.002）, and burnt sien- na （P = 0.001）. No significant differences in the rate of detection were observed according to velocity for light grayish pink （P = 0.643） or dark brown （P = 0.396）. CONCLUSION： SBI sensitivity was affected by back- ground color and capsule passage velocity in the models. These findings may facilitate the rapid detection of bleeding lesions by CE.

Vehicle detection algorithm based on codebook and local binary patterns algorithms

Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment.Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns(LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.

Moving object detection method based on complementary multi resolution background models

A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.

RCS analysis in the detection of a generic missile target with sea clutter background

The detection of a missile target in heavy sea clutter is a significantly challenging problem due to the clutter effects. In this paper, the radar cross sections(RCS) of a pre-assumed generic missile model is computed with multilevel fast multi-pole algorithm(MLFMA), while the RCS of ocean surface is computed by a more reduced form of the fractional Weierstrass scattering model proposed here. At last, the computed RCS of missile model is compared with that of sea surface, and then the comparisons of missile-to-ocean RCS ratios of different incident angles, incident frequencies, and polarization patterns are also presented. The discussion and comparisons of RCS of the missile and ocean surface can help us to plan and design a radar system in the application of detection of a missile target or other analogous weaker targets in the strong sea clutter background.