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.

DEVELOPMENT OF MOVING TARGET DETECTION AND IMAGING BY AIRBORNE SAR

The detection and ima ging of moving targets based on airborne synthetic aperture radar (SAR) is a cru cial technique for the modern radar. Firstly, the mathematical model of SAR ech o signal which comes from moving targets is constructed. Based on this model, th e features of moving target imaging are introduced and the effects of target mov ement to SAR imaging are analyzed. Then the development and the status of this t echnique are reviewed in detail. Finally, some frontiers of this field are point ed out.

A camouflage target detection method based on local minimum difference constraints

To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(LMDC).The algorithm first uses the mean to optimize the initial background model,removes the stable background region by global comparison,and extracts the edge point set in the potential target region so that each boundary point(seed)grows along the center of the target.Finally,we define the minor difference constraints term,combine the seed path and the target space consistency,and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection.The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms.The experimental results show that the proposed algorithm yields good results based on integrity,accuracy,and a number of objective evaluation indexes,and its overall performance is better than that of the compared algorithms.

Detection technique of moving target based on passive millimeter wave

To solve the problem of insufficient ability when detecting the high-speed moving target with passive millimeter wave technology, a direct-detection passive millimeter wave detecting system using the monolithic microwave integrated cir- cuit （MMIC） millimeter wave radiometer is built, and the measured data are obtained by experiment under different condi- tions. Based on feature analysis of testing signals, it points out that the peak of the first pulse and interval of two peak pulses are valid features which can reflect the motion characteristic of target. A method to calculate the moving speed of target is put forward. The calculating results indicate that the proposed method has enough accuracy and is feasible to determine the parameters of the moving target using for passive millimeter wave system.

Method of moving target detection based on sub-image cancellation for single-antenna airborne synthetic aperture radar

The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented. First the subimage is obtained through frequency processing is pointed out. The imaging difference of a stationary objects and moving object in the subimage based on the frequency division is analyzed from the fundamental principle. Then the developed method combines the shear averaging algorithm to focus on the moving target in the subimage, after the clutter suppression and the focusing position in each subimage is obtained. Next the observation model and the relative movement of the moving targets between the subimages estimate the moving targets. The theoretical analysis and simulation results demonstrate that the method is effective and can not only detect the moving targets, but also estimate their motion parameters precisely.

Moving target detection based on improved ghost suppression and adaptive visual background extraction

Visual background extraction algorithm(ViBe)uses the first frame image to initialize the background model,which can easily introduce the“ghost”.Because ViBe uses the fixed segmentation threshold to achieve the foreground and background segmentation,the detection results in many false detections for the highly dynamic background.To solve these problems,an improved ghost suppression and adaptive Visual Background Extraction algorithm is proposed in this paper.Firstly,with the pixel’s temporal and spatial information,the historical pixels of a certain combination are used to initialize the background model in the odd frames of the video sequence.Secondly,the background sample set combined with the neighborhood pixels are used to determine a complex degree of the background,to acquire the adaptive segmentation threshold.Thirdly,the update rate is adjusted based on the complexity of the background.Finally,the detected result goes through a post-processing to achieve better detection results.The experimental results show that the improved algorithm will not only quickly suppress the“ghost”,but also have a better detection in a complex dynamic background.

Improved TQWT for marine moving target detection

Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wavelet transform(TQWT)for moving target detection.Firstly,this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform(FRFT)domain.Then,TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target’s oscillation characteristics,using the basis pursuit denoising(BPDN)algorithm to get the wavelet coefficients.Furthermore,an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target.Finally,experiments on the Council for Scientific and Industrial Research(CSIR)dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.

An algorithm for moving target detection in IR image based on grayscale distribution and kernel function

A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.

A Space-Time Reverberation Model for Moving Target Detection

In recent years,moving target detection methods based on low-rank and sparse matrix decomposition have been developed,and they have achieved good results.However,there is not enough interpretation to support the assumption that there is a high correlation among the reverberations after each transmitting pulse.In order to explain the correlation of reverberations,a new reverberation model is proposed from the perspective of scattering cells in this paper.The scattering cells are the subarea divided from the detection area.The energy fluctuation of a scattering cell with time and the influence of the neighboring cells are considered.Key parameters of the model were analyzed by numerical analysis,and the applicability of the model was verified by experimental analysis.The results showed that the model can be used for several simulations to evaluate the performance of moving target detection methods.

A New Method of Small Moving Target Detection and Its Performance Analysis

This paper describes a new method of small moving target detection and analyzes the performance of this algorithm. The method is based on multi-level threshold decision-making and sliding trajectory confidence testing technology. The parameters of the algorithm are also given. Experiments have been conducted, the results show that the algorithm has advantages of high detection probability, simple structure, and excellent real-time performance.

Moving Target Detection and Tracking for Smartphone Automatic Focusing

In this paper,a non-contact auto-focusing method is proposed for the essential function of auto-focusing in mobile devices.Firstly,we introduce an effective target detection method combining the 3-frame difference algorithm and Gauss mixture model,which is robust for complex and changing background.Secondly,a stable tracking method is proposed using the local binary patter feature and camshift tracker.Auto-focusing is achieved by using the coordinate obtained during the detection and tracking procedure.Experiments show that the proposed method can deal with complex and changing background.When there exist multiple moving objects,the proposed method also has good detection and tracking performance.The proposed method implements high efficiency,which means it can be easily used in real mobile device systems.

Automatic Detection of Moving Target with an Active Camera

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NOVEL METHOD OF MOVING TARGET DETECTION FOR DUAL-CHANNEL WAS RADAR BASED ON COMPRESSED SENSING

We propose a ground moving target detection method for dual-channel Wide Area Surveillance(WAS) radar based on Compressed Sensing(CS).Firstly,the method of moving target detection of the WAS radar is studied.In order to reduce the sample data quantity of the radar,the echo data is randomly sampled in the azimuth direction,then,the matched filter is used to perform the range direction focus.We can use the compressive sensing theory to recover the signal in the Doppler domain.At last,the phase difference between the two channels is compensated to suppress the clutter.The result of the simulated data verifies the effectiveness of the proposed method.

A PARAMETER ESTIMATION FOR DETECTION AND IMAGING OF MOVING TARGETS WITH SAR BY WVD

Based on a joint time-frequency two dimensional processing, this paper proposes a method for the detection and imaging of moving targets SAR by using Wigner-Ville Distribution (WVD). It is a parameter estimation method to generate a high resolution image. The problem of WVD in dealing with multi-point targets and extended targets are also discussed. The computer simulation results illustrate its availability.

Detection of Moving Targets Using Soliton Resonance Effect

We present a soliton resonance method for moving target detection which is based on the use of inhomogeneous Korteweg-de Vries equation. Zero initial and absorbing boundary conditions are used to obtain the solution of the equation. The solution will be soliton-like if its right part contains the information about the moving target. The induced soliton will grow in time and the soliton propagation will reflect the kinematic properties of the target. Such soliton-like solution is immune to different types of noise present in the data set, and the method allows to significantly amplify the simulated target signal. Both, general formalism and computer simulations justifying the soliton resonance method capabilities are presented. Simulations are performed for 1D and 2D target movement.

A Novel System for Moving Object Detection Using Bionic Compound Eyes

Conventional moving target detection focuses on algorithms to improve detection efficiency. These algorithms pay less attention to the image acquisition means, and usually solve specific problems. This often results in poor flexibility and reus- ability. Insect compound eyes offer unique advantages for moving target detection and these advantages have attracted the attention of many researchers in recent years. In this paper we proposed a new system for moving target detection. We used the detection mechanism of insect compound eyes for the simulation of the characteristics of structure, control, and function. We discussed the design scheme of the system, the development of the bionic control circuit, and introduced the proposed mathe- matical model of bionic cqmpound eyes for data acquisition and object detection. After this the integrated system was described and discussed. Our paper presents a novel approach for moving target detection. This approach effectively tackles some of the well-known problems in the field of view, resolution, and real-time processing problems in moving target detection.

AN IMPLEMENTATION METHOD OF ADAPTIVE THRESHOLD TO DETECT MOVING TARGETS BASED ON ATI TECHNOLOGY

AT-InSAR(Along Track Interferometric SAR) is a technique to detect slow-moving targets. However, the detection performance is greatly influenced by noise and clutter. In this paper, the influence of noise and clutter on the detecting performance is analyzed. By simulating different background clutter and noise, the performances of the phase threshold and dual-threshold methods are discussed in detail, and then the adaptive-threshold method is proposed which can greatly improve the detection performance.

Small tracking error correction for moving targets of intelligent electro-optical detection systems

Small tracking error correction for electro-optical systems is essential to improve the tracking precision of future mechanical and defense technology.Aerial threats,such as“low,slow,and small(LSS)”moving targets,pose increasing challenges to society.The core goal of this work is to address the issues,such as small tracking error correction and aiming control,of electro-optical detection systems by using mechatronics drive modeling,composite velocity–image stability control,and improved interpolation filter design.A tracking controller delay prediction method for moving targets is proposed based on the Euler transformation model of a two-axis,two-gimbal cantilever beam coaxial configuration.Small tracking error formation is analyzed in detail to reveal the scientific mechanism of composite control between the tracking controller’s feedback and the motor’s velocity–stability loop.An improved segmental interpolation filtering algorithm is established by combining line of sight(LOS)position correction and multivariable typical tracking fault diagnosis.Then,a platform with 2 degrees of freedom is used to test the system.An LSS moving target shooting object with a tracking distance of S=100 m,target board area of A=1 m^(2),and target linear velocity of v=5 m/s is simulated.Results show that the optimal method’s distribution probability of the tracking error in a circle with a radius of 1 mrad is 66.7%,and that of the traditional method is 41.6%.Compared with the LOS shooting accuracy of the traditional method,the LOS shooting accuracy of the optimized method is improved by 37.6%.

Dynamic Target Detection and Tracking Based on Quantum Illumination LIDAR

In the detection process of classic radars such as radar/lidar,the detection performance will be weakened due to the presence of background noise and loss.The quantum illumination protocol can use the spatial correlation between photon pairs to improve image quality and enhance radar detection performance,even in the presence of loss and noise.Based on this quantum illumination LIDAR,a theoretic scheme is developed for the detection and tracking of moving targets,and the trajectory of the object is analyzed.Illuminated by the quantum light source as Spontaneous Parametric Down-Conversion(SPDC),an opaque target can be identified from the background in the presence of strong noise.The static objects obtained by classical and quantum illumination are compared,respectively,and the advantages of quantum illumination are verified.The moving objects are taken at appropriate intervals to obtain the images of the moving objects,then the images are visualized as dynamic images,and the three-frame difference method is used to obtain the target contour.Finally,the image is performed by a series of processing on to obtain the trajectory of the target object.Several different motion situations are analyzed separately,and compared with the set object motion trajectory,which proves the effectiveness of the scheme.This scheme has potential practical application value.

SURVEILLANCE OF MOVING TARGET BEHIND PARTITIONS USING A UWB-TWDR

This paper firstly analyzes the property of the low frequency electromagnetic wave, which can penetrate many types of non-metallic materials, and the ability of Ultra-Wide Band (UWB) impulse signal which has high range resolution. Then the methods are discussed for conducting surveillance through walls, detecting and locating the moving persons behind the partitions. The schematic diagram of Through-Wall Detecting Radar (TWDR) and the models of moving target are shown and the principle of detecting the moving target is also provided with coherent superimposing technique on a range gate. Finally an algorithm for estimating the location of targets is given. The performance of TWDR is validated by the experiments of penetrating a wood block, a red brick wall and a reinforced concrete wall.