Moving Target Detection Algorithm Research Articles

Target motion detection algorithm based on dynamic threshold

Realizing moving target detection through visual algorithms is a major branch of computer technology. Moving target detection has a wide range of applications in many fields such as video surveillance, unmanned driving, and behavior research. The current algorithm has been able to achieve moving target detection well in a static background, but the detection of multiple moving targets in a dynamic background is still a challenging research area. The main pain points are complex background, light changes, noise interference and Occlusion and real-time detection requirements, etc. Aiming at the detection of moving targets in complex scenes, a moving target detection algorithm based on dynamic threshold is proposed. The algorithm first uses the gray difference gradient of three adjacent frames of the video sequence to calculate the segmentation threshold of foreground moving targets to realize the dynamic threshold. Then use dynamic thresholds to achieve multi-target motion detection in complex backgrounds or scenes with changing lighting. Because the gradient change of adjacent frames of the scene is considered in the process of moving target detection and segmentation, and the gradient change is converted into a dynamic threshold to participate in moving target detection and segmentation, the algorithm can adapt to scenes with changing lighting. Experiments show that this algorithm is feasible for moving target detection in complex scenes, and has a lower error rate than that of traditional algorithms, and is more suitable for to complex or changing lighting scenes.

A Modified Radon Fourier Transform for GNSS-Based Bistatic Radar Target Detection

The Global Navigation Satellite System (GNSS)-based passive bistatic radar (PBR) which uses the GNSS signal as the illuminators of opportunity is studied for moving target detection (MTD). GNSS-based PBR has many advantages due to the removal of the transmitting device; however, its fundamental limitation is the low power density of the GNSS signal. Therefore, the integration time should be sufficiently long to obtain a promising maximum detectable range. On the other hand, the integration time is limited by the range migration and Doppler migration of the echo caused by target motion. In this letter, a novel MTD algorithm is proposed for the GNSS-based PBR, by employing a modified radon Fourier transform (MRFT) to achieve the required long-time integration for moving targets. The MRFT integrates the echo energy via joint searching of range, Doppler, and Doppler rate of the target, which can handle not only the range migration but also the Doppler migration problems, and significantly improves the signal-to-noise ratio (SNR) of the echo signal. An experiment using the GPS L5 signal as the illumination source is conducted and a moving car is successfully detected by the proposed algorithm, although significant range migration and Doppler migration are present due to variation of its speed.

RETRACTED: Swimming target detection and tracking technology in video image processing

The new system is presented for target detection swimming, tracking multiple targets in a video sequence. Since the output of the system is based on target motion, to use background subtraction to move the area within each video frame of the first segment foreground and background. A new method proposed for detecting drowning in a person swimming in a data using video footage. The proposed Swimming target detection and tracking technology based on Discrete Cosine Transform Algorithm, Target Detection Algorithm, and Traditional Cam-Shift Algorithm. For background extraction and updating, use the frame-by-frame differential Discrete Cosine Transform Algorithm used to the exact motion region of the entire video section in image processing technology based analysis. Static and dynamic feature detection identification normal swimmers and drowning swimmers. Swimming-based data are collected in real time using a camera on the surface of the water, including a video image sequence with the main observation swimming and behavior analysis based system immersion and three steps to detect swimming. By combining frame subtraction and background subtraction, the advanced moving Target Detection Algorithm can detect the full area of interest. Then, with average particle filtering, analysis of Traditional Cam Shift Algorithm, it can automatically select the target for the resulting detection results and introduce a new person tracking method. Swim target detection and track results to analyze motion parameter estimation algorithms. Finally, the purpose of the set of human diagnostics and follow-up tests is to test algorithmic efficiency.

基于道路边缘运动障碍物检测算法的农业导航机器人道路识别技术

In order to recognize the road effectively, agricultural robots mainly rely on the tracking and detection data of road obstacles. Traditional obstacle detection mainly studies how to use multiple fusion methods such as vision and laser to analyse structured and simplified indoor scenes. The working environment of agricultural robots is a typical unstructured outdoor environment. Therefore, based on the environmental characteristics of agricultural robot navigation, the mean displacement algorithm is introduced to detect and study the obstacles aiming at the road edge. After explaining the advantages and principle flow of the mean displacement algorithm to effectively realize motion capture, the feasibility of target location and tracking research is discussed. After that, the bottom data acquisition and analysis model is constructed based on the road navigation data of agricultural robots. To capture the movement obstacles of road edge and build the foundation of road recognition technology. In order to improve the effectiveness of motion obstacle capture and detection, a moving target detection algorithm is proposed to optimize and update the mean displacement algorithm, and constructs a feature-oriented hybrid algorithm motion capture model. The simulation results indicate that the proposed optimization model can effectively improve the tracking efficiency of non-rigid targets in outdoor environment, and the number of evaluation iterations can reach 3.5621 times per frame, which shows that the research has good theoretical and practical value.

A Comparative Study of Moving Target Detection Algorithms

This paper wants to analyze and compare the mainstream algorithms for moving target detection and lay a foundation for algorithm improvements as well as for such research directions as intelligent transportation system and traffic calculation, this paper selects three target detection algorithms for comparative study: methods of interframe difference, background difference and optical flow. It conducts simulation experiment on traffic surveillance videos with MATLAB programming, selects the threshold for frame difference method suitable for the current video and improves detection accuracy by binaryzation, expansion, corrosion and other processing methods. Interframe difference method compares the effects between the original difference image and grayscale difference image and considers bad difference caused by excessive moving distance. Background difference method compares the effect images after morphological processing, and reduces the impact of noises. The advantages are shown by comparing the original image with the labeled optical flow diagram using the optical flow method. By comparing the recognition effect images and processing time of these three algorithms, it analyzes and concludes the strengths and weaknesses as well as the ranges of application in intelligent transportation system.

Real-Time Detection and Spatial Segmentation of Difference Image Motion Changes

In this article, real-time detection of moving targets is performed, and an improved moving target detection algorithm combining background modeling and three-frame difference method is studied. An improved anisotropic differential filtering algorithm is proposed. The algorithm mainly compares the gradient difference between the target and the background in eight nearby directions. Choose the average of the three directions with the smallest spread function value to filter the space, which will effectively highlight the difference between the targets. so that the target signal is well preserved in the differential space domain; then based on obtaining the difference map. The average gray level of the target and the local signal-to-noise ratio of the spatial domain obtained by the energy enhancement algorithm combining the spatial and temporal motion characteristics have reached 278 and 14.84 dB, which further improves the target signal. Strict registration of heterogeneous spatial domains is completed by affine transformation and bilinear interpolation, and in many cases, the invariance and robustness of the method are verified. Focus on the proposed color space fusion method based on spatial frequency and fuzzy transformation. The superiority of the methods is evaluated and compared from both subjective and objective aspects. Finally, it lays a good foundation for moving target detection and better dealing with complex scenes. The improved inter-frame difference method combined with background subtraction validates and compares the method in different scenarios. It shows the advanced nature of the method in both qualitative and quantitative aspects. And effectively overcome the shortcomings of the particle filter tracking algorithm based on a single feature.

Intelligent Video Surveillance of Tourist Attractions Based on Virtual Reality Technology

This question combines virtual reality technology with video surveillance technology to realize an intelligent video surveillance system based on virtual reality technology in tourist attractions. It analyzes the technology and theory involved in the video surveillance system based on virtual reality technology and then designs the architecture and functions of the system. The system is divided into three parts, such as local monitoring client, streaming media server, and remote monitoring client. It detailed the design and implementation of each module. We use the OpenCV library and moving target detection algorithm to realize the intelligent analysis of surveillance video and use the OpenGL engine to render the surveillance video of VR panorama of tourist attractions to achieve the effect of virtual reality. The video surveillance system implemented is tested and analyzed in combination with the actual application of tourist attractions. The test results show that the system can operate stably and meet the design indicators. The remote monitoring delay is controlled within one second. The remote monitoring client has achieved the effect of virtual reality.

Small-scale moving target detection in aerial image by deep inverse reinforcement learning

It proposes a deep inverse reinforcement learning method for slow and weak moving targets detection in aerial video. Differential gray images of adjacent frames are used as the network model input, and the feature network layer extracts the candidate moving target regions through the multi-layer convolution. The candidate target information is used as the initial layer of the policy network. The expert trajectory is used to adjust and optimize the feature convolution network model and the policy fully connected network model to realize the training the reward return function and the expert policy. In the stage of autonomous improvement policy, the policy model is re-optimized by unmarked aerial video, and deep inverse reinforcement learning and nonlinear policy network are used to make decision on moving target position and size information. The target size of the multi-group aerial video test set is 10 * 10 pixels. Experimental results show that the proposed algorithm has the advantage of the nonlinear policy of the neural network compared with the traditional moving target detection algorithm, and the detection result is more accurate. At the same time, compared with the traditional marginal programming (MMP) method and the structured classification based (SCIRL) method, the proposed algorithm shows obvious advantages in the accuracy of aerial video moving target detection.

SAR ground moving target's along‐track velocity estimation in the complex image domain via SoWVD

A ground moving target is always defocused in the synthetic aperture radar (SAR) image domain due to the along-track velocity. In the image domain, a moving target's complex response along the azimuth direction can be modelled as a linear frequency-modulated signal, therefore, its Doppler rate, as well as the along-track velocity, can be estimated based on the recently proposed second-order Wigner–Ville (SoWVD) distribution transform. The proposed method has a low computational complexity without the search procedure and can be well combined with the SAR ground moving target detection algorithms in image domain, where the signal to clutter-plus-noise ratio is higher than other domains. Finally, the effectiveness of this method is verified with some numerical experiments.

A new moving target detection algorithm

The traditional GMM can easily detect the background as a moving target and is sensitive to the illumination changes. So this paper put forward a moving target detection algorithm combining the improved custom GMM with the five-frame interframe difference method. After the video image sequence is preprocessed, the GMM is added with the balance coefficient and the merged useless Gaussian distribution is combined. Then the improved GMM is combined with the five-frame interframe difference method to obtain the detected target. Simulation experiments show that the improved algorithm adapt the environment better. The improved algorithm reduces the impact of illumination changes on moving target detection, and significantly reduces the false detection rate.

Moving target detection algorithm based on spatiotemporal correlation multi-channel clustering

<sec>In the process of tracking target, certain multi-modal background scenes are not suitable for the off-line training model, and moving target detection is affected as background in the current video environment is mostly multi-modal scene with much noise, and the characters of moving targets irregularly change, which,therefore, requires a more stable and robust moving target detection algorithm. To solve this problem, taking advantage of spatiotemporal relationship learning, the mixed Gaussian model (GMM) is improved in three aspects.</sec><sec>First, the initialization method combining five-frame difference and intra-frame neighborhood average is proposed to obtain the initial parameters of the mixed Gaussian model. The five-frame difference method is introduced to obtain the initial parameters of the model, so that the background model is closer to the real scene. The intra-frame neighborhood average value is introduced, and an accumulation matrix CA is proposed to record the number of neighboring pixel points, then to enhance the information relevant to the neighborhood. This process can reduce the discontinuity of the target. </sec><sec>Second, the calculation method of the neighborhood correlation is introduced to update the parameter of Gaussian model. Since the single pixel feature is related to the neighborhood random correlation, the random subsampling technology and neighborhood spatial propagation theory are combined together, and the execution efficiency is taken into account to simplify the process of updating model. To speed up the model convergence, an observation vector is built in the time dimension to optimize the model parameters, and the weight <i>ω</i> is gained based on the posterior probability. </sec><sec>Then, the color-gradient method incooperated with the color HSI space and gradient information is adopted in this paper to complete the multi-channel Gaussian mixture model. The initial and the updated parameters of the Gaussian model in each channel can be acquired via the above steps. To simplify the computation of three channels, the random sampling of background pixels is introduced. Finally the detection of moving targets in complex environments is realized. </sec><sec>The experiments show that the proposed algorithm has a great improvement in suppressing the influence of complex background and detecting target integrity, and the influence of the moving target in the initial stage is eliminated.</sec>

Ground moving target detection algorithm for monopulse‐SAR based on complex monopulse ratio

Monopulse processing has been proved to be an effective way for airborne synthetic aperture radar (SAR)/ground moving target indication. In the monopulse-SAR system, moving target detection is applied to a complex ratio diagram, referred to as a complex monopulse ratio diagram (CMRD), generated through a pixel-by-pixel comparison between the range-Doppler (RD) spectra obtained from the difference and sum channels. This study examines the statistics of complex monopulse ratio (CMR) in the Gaussian background. Based on the statistics, a new random variable termed normalised CMR (NCMR) is derived by a linear transform to CMR. It is demonstrated that the conditional probability density function of NCMR, under the null hypothesis ( H 0 ), is independent of the clutter-plus-noise environment. Motivated by this characteristic, a constant false alarm rate detector using the modulus of NCMR as the test statistic is proposed to detect moving targets. The detection performance is evaluated with a closed-form expression of the probability of false alarm ( P FA ) derived. Moreover, the construction of the detector is extended to a so-called multi-CMRD form to accommodate even higher detection performance. Experimental results from two groups of real data are displayed to validate the statistical analysis and verify the detection performance.

Moving Target Shadow Detection and Global Background Reconstruction for VideoSAR Based on Single-Frame Imagery

The indication of moving targets with low velocities, which is an extremely serious problem for traditional ground moving targets indication (GMTI), can be achieved relatively easily by shadow detection in VideoSAR imagery. Numerous image and video processing technologies have been applied to shadow detection in VideoSAR to improve the performance for GMTI. Among these processing technologies, background modeling is the key technology, which has received lots of research based on multi-frame imagery but little on single-frame imagery. This paper introduces the formation of the moving target shadows and adds the concepts of occlusion, umbra, and penumbra to prior work. In addition to this, the paper proposed a local feature analysis method based on single-frame imagery, which can accurately detect moving target shadows. Based on the result of this method, the background model can be reconstructed using single-frame imagery, which avoids the ”ghost” phenomenon in moving target detection algorithms based on multi-frame imagery. Finally, we replace the initial frame in the visual background extractor (ViBe) algorithm by the reconstructed background model we get, and the result shows that the ghosts are removed effectively.

无人机视频图像运动目标检测算法综述

无人机视频图像运动目标检测算法综述

Research on Improved Adaptive ViBe Algorithm For Vehicle Detection

Abstract Vehicle detection is an important step in vehicle tracking and recognition in video environment. Vibe algorithm is a moving target detection algorithm based on background difference method. Based on the traditional ViBe algorithm, this paper introduces three-frame difference method combined with ViBe algorithm to speed up the elimination of ghosts, and proposes an adaptive Vibe algorithm, which defines two kinds of vehicle detection errors and their corresponding error functions. Then, according to the range of these two errors, a set of reasonable judgment methods are determined to adjust the unreasonable threshold, which ensures the adaptive updating of the background model. It improves the environmental adaptability of vehicle detection and ensures higher accuracy of vehicle detection under complex illumination conditions.

Motion Speed Detection Algorithm Based on the Control Platform of Automatic Optical Inspection Systems for PCB

This paper mainly introduces the motion speed algorithm for control platform of automatic optical Inspection for PCB. It mainly gives a comparison of the existing moving target detection methods, and proposes an improved moving target detection algorithm, the movement target labeling point video stream detection method. The method obtains good detection results by detecting the motion speed for control platform of the automatic optical Inspection Systems, and improves the detection efficiency.

Multi-Scale Motion Attention Fusion Algorithm for Video Moving Target Detection

Motion Target detection is a key technology in video analysis, this paper propose a target detection algorithm based on multi-scale motion attention analysis, which provides a new method for motion target detection under global motion scene; Firstly, the noise of motion vector field is removed by filter, and according to the mechanism of visual attention, spatial-temporal motion attention model is built, then the trust degree of motion vector is suggested on the basis of validity analysis of motion vector, and decision fusion of multi-scale motion attention is carried by D-S theory to detect region of motion target; The test result of different video shows the algorithm is effectively for target detection under global motion scene, overcome the defect of traditional algorithm and has better robustness than other algorithms.

Moving Target Detection Algorithm for Forest Fire Smoke Recognition with Improved ViBe

In order to discover the forest fire at the early stage, the video based fire smoke detection system should be developed. This paper proposes an improved ViBe algorithm to overcome the drawbacks of ViBe which is widely used in forest fire smoke recognition. To make the algorithm faster, the improved ViBe algorithm introduces a switch variable to control the model updating every 2 frames. Also, the new ViBe algorithm contains a method to detect if the scene is changing, and upon finding the changing, a fast updating strategy would be used to increase the adaptive ability. The experiments are done from 2 aspects, to test the time consuming and adaptive ability of the improved algorithm. The experiments results show that the improved ViBe algorithm has better performance compared to ViBe algorithm and GMM algorithm, and can detect the forest fire smoke accurately in different conditions.

Data level moving target detection algorithm based on B‐RFS

Under low signal-to-clutter ratio (SCR) condition, the detection performance of traditional detection algorithm that detected in the signal level is poor, especially for moving the target. In this study, a new detection idea to detect in the data level is presented to improve the detection performance under low SCR condition. The new detection algorithm directly processes the measurement set generated by pre-processing. On the basis of a binary hypothesis and the Bernoulli random finite set (B-RFS), the authors proposed a B-RFS detector, in which the probability of existence of target is treated as a test statistic of the binary hypothesis. As the information of multi-frame is used, the detection performance will be better than the traditional detection algorithm. If the presence of the target is determined by the detector, they can obtain the estimation of the target state. The simulation results show that the proposed algorithm has better performance than traditional detection algorithm, and the performance of the algorithm is better with the increase of the processing time. Furthermore, the estimation of the target state is close to the true target state. As the proposed algorithm is detected in the data level, it can be applied to a variety of sensors.

Improved axis rotation MTD algorithm and its analysis

In radar detection, weak targets’ range migration often happens during long time integration. To detect weak targets effectively, an improved axis rotation moving target detection (IAR-MTD) is introduced and analysed in detail. IAR-MTD can detect weak targets by compensating the linear part of range migration via the axis rotation and coherently integrating the echoes via moving target detection (MTD). Then the realization of IAR-MTD is derived. Furthermore, the coherent integration gain of IAR-MTD is analysed, which is better than that of traditional MTD, Radon–Fourier transform (RFT) and Keystone transform (KT). Subsequently, to decrease the computational complexity of IAR-MTD, some suggestions are given. Besides, unambiguous Doppler estimation, the tolerance of acceleration, and the multi-target detection of IAR-MTD are analysed respectively. Finally, some numerical experiments are provided to show the performance of IAR-MTD in different conditions and testify the advantages of IAR-MTD over MTD, RFT and KT. The result indicates that IAR-MTD may effectively detect the weak moving targets with constant radial velocity and it is compatible with MTD radar system.