111results about How to "Improve target tracking accuracy" patented technology

An early stage tumor localizing tracking based on the multimode sensitization imaging fuse, belongs to the medical image processing field. The invention includes: a medical image before the operation for obtaining the tumour aim focus imaging sensitization; an ultrasound sensitization image during the operation for obtaining the tumour aim focus imaging sensitization. When the image is processed the guide therapy, using the global rigid transformation and the local nonstiff transformation combination round the tumour aim focus as the geometric transformation model with deformation registration, the sensitization images before and during the operation are processed with the deformation registration based on the union marked region, while the images before and during the operation are fused, to rebuild the three-dimensional visualization model in the tumor focus region. Using the above deformation registration method to complete the sport deformation compensation for the imaged before the operation, the target tracking of the tumour target focus is further automatically completed. The invention can be used in a plurality of places, such as the early diagnosis of the tumour, the image guide tumour early intervention, the image guide minimal invasive operation, the image guide physiotherapy etc.

The t invention discloses an improved multi-expansion target tracking method. The method comprises the following steps: carrying out clustering and division on a measurement data set of a sensor and initializing a probability hypothesis density value and a potential distribution value of a target; predicting and updating a probability hypothesis density value and a potential distribution value oftarget state set at a next time to obtain a probability hypothesis density value and a potential distribution value at the current time; carrying out trimming and combination on a Gaussian term of anintensity function of the target, extracting target state estimation, and carrying out performance evaluation; and carrying out prediction, updating, and trimming and combination repeatedly and tracking the target till disappearance of the target. Therefore, the tracking accuracy of the target in a clutter environment is improved; the precision losses caused by the blind area of the radar are reduced; the computing load of the filter is reduced; and the engineering application of the GM-CPHD filter is realized well.

The invention provides a detection system for detecting targets around a vehicle and application of the detection system. The detection system includes a vehicle front forward direction sensor (1), a vehicle roof forward direction sensor (2), a right front sensor (3), a left front sensor (4), a right rear sensor (5), a left rear sensor (6), a rear sensor (7), a left side sensor (8), a right side sensor (9) and target selection modules (10), wherein the target selection modules (10) include a forward direction target selection module (11) and a side direction and backward direction target selection module (12), wherein the forward direction target selection module is configured to select targets at least four positions in front of the vehicle as vehicle front side tracking targets based on received target motion information, and the side direction and backward direction target selection module is configured to select targets at the side direction and backward direction of the vehicle as vehicle side direction and backward direction tracking targets according to received target motion information.

The invention belongs to the technical field of photoelectric tracking, precision measurement, navigation and location and particularly relates to a target tracking method based on intelligent IMM Kalman filter feedforward compensation and a system. A neural network intelligent target recognizer recognizes the type of a tracked target, state equation parameters of online operation of an improved intelligent IMM Kalman filter are adjusted, so that the filter is endowed with an adaptive characteristic, an online operating state model equation is matched with the tracked target, multiple filtersbased on different models are used for parallel work, speed and acceleration of the maneuvering target are estimated in real time accurately, estimation accuracy higher than that of traditional filtering is obtained, and a feedforward compensation controller constructed on the basis can obtain higher feedforward compensation precision and control performance.

The present invention provides a multi-target tracking method after through-wall radar imaging, the rapid imaging can be achieved in a certain error, and a high quality imaging result can be obtained. According to the target targeting algorithm based on two-dimensional low-pass filtering and image etching, measurement position information and amplitude information can be effectively extracted, and finally a nearest area data association algorithm is used to carry out target tracking. At the same time, according to an interacting multiple model tracking algorithm, the high mobility of the target is fully considered, and the target tracking accuracy is high.

A satellite laser distance measurement system based on tilt calibration is featured as setting high speed tilt reflector in telescoping unit, using tilt tracking transducer to carry out wave front detection on light wave received by receiving telescope, outputting calibration control signal to high speed tilt mirror in telescope by high speed processor after error signal is restored, starting up satellite laser distance measurement system in telescoping unit to send out laser with specific wavelength for carrying out satellite laser distance measurement after tilt closed loop real time calibration is finished.

The invention belongs to the technical field of image processing, and relates to a multi-source target tracking measurement system and tracking method based on active vision. The system comprises a photoelectric assembly and a stable platform, wherein the photoelectric assembly is composed of a visible light camera, an infrared thermal imager, a laser range finder, an information processor and a photoelectric cabin structure; the visible light camera, the infrared thermal imager, the laser range finder and the information processor are all installed inside the photoelectric cabin structure; the stable platform consists of an orientation motion assembly, a pitching motion assembly and a control assembly; and the control assembly receives an instruction sent by the information processor, andcontrols the orientation motion assembly and the pitching motion assembly to enable visual axes to stably point to a target. The invention further provides the multi-source target tracking method based on the active vision. The orientation motion assembly and the pitching motion assembly are driven to rotate according to the control instruction, so that the visual axis of the visible light cameraand the visual axis of the infrared thermal imager point to the target, and the target is always kept in the center of a shot image.

The invention belongs to the technical field of computer vision, and particularly relates to a target tracking method based on a Siamese FC framework and a PFP neural network, which comprises the following steps: (1) based on the Siamese FC framework, processing a target area in a first frame of a video to obtain template characteristics; Inputting the template features into a PFP neural network to obtain template final features; (2) based on a Siamese FC framework, performing t-detection on the t- Processing the target area in one frame to obtain a search area feature; Inputting the featuresof the search area into a PFP neural network to obtain final features of the search area; (3) taking the final feature of the template as a convolution kernel, performing convolution on the final feature of the search area, and determining a central position of a tracking target in the t-th frame and a target area; And (4) repeating the step (2) and the step (3) until the video is finished, and completing the tracking of the central position of the tracking target and the target area. According to the invention, the context information and different receptive field information can be fused, sothat the target tracking precision is improved.

A method and system is proposed for use by a moving station (such as a jetfighter) for radar tracking of a moving target (such as an air-to-air missile). The proposed method and system involves the use of a hybrid FSK/LFM (Frequency Shift Keying & Linear Frequency Modulation) scheme for acquiring a collection of raw radar data, a first Gaussian-noise filter array of one-stage linear Kalman filters for S/N-enhancement of the raw radar data, a trilateration module, and a second Gaussian-noise filter array of one-stage linear Kalman filters for S/N-enhancement of the trilateration-resulted radar data. These features allow the radar tracking of moving objects to be more fast and accurate.

The invention discloses an underwater target cooperative tracking method based on consistency estimation and dormancy scheduling. The method comprises the following steps: disposing two types of underwater sensor nodes, and arranging that the nodes are at a dormancy state with a low-frequency reflection detection function at initial time; the dormancy sensor nodes periodically determining whether to be converted into an activation state; sensors entering a wakeup state sending water sound pulse signals for detecting a target and accordingly determine the position of the target; for the purpose of obtaining target position information with quite high precision, performing consistency estimation on observation information of the two kinds of nodes; and according to a predicted target motion locus, dynamically activating the sensors disposed at a target prediction path area, setting work cycle periods and duty ratios for the nodes, and at the same time, underwater dynamic sensors dynamically adjusting positions so as to realize continuous and dynamic tracking of the target. The method provided by the invention has the advantages of improving the target tracking precision, reducing the energy consumption, prolonging the network life and the like.

The invention discloses a monitoring target linkage tracking control method and device and a storage device. The method comprises the following steps: acquiring an initial image and initial coordinates of a monitoring target; controlling a first monitoring camera device and a second monitoring camera device to track and shoot the monitoring target according to the initial coordinates; acquiring afirst tracking image of the monitoring target tracked and shot by the first monitoring camera device, and a second tracking image of the monitoring target tracked and shot by the second monitoring camera device; determining a first similarity value between a first monitoring target in the first tracking image and the initial image of the monitoring target, and determining a second similarity valuebetween a second monitoring target in the second tracking image and the initial image of the monitoring target; and adjusting the first monitoring camera device and the second monitoring camera device according to a similarity comparison result. In this way, the purposes of improving the target tracking precision and preventing the target from being lost can be achieved.

The invention relates to a joint resource optimization method for multi-target position estimation in a distributed MIMO radar system. The joint resource optimization method comprises: a target is designated and a minimized maximum value of a multi-target position estimation error is used as a target function; under the circumstances that the total numbers of the transmitting and receiving array elements are limited and the transmitted power is given, a resource optimization model of combination of transmitting and receiving array element selection and power allocation is established; and on the basis of a heuristic search algorithm and a continuous parameter convex approximation algorithm, a resource joint allocation algorithm based on cycle minimization is proposed to solve a hybrid Boolean type joint optimization problem to obtain a joint resource allocation result. According to the invention, the energy relationship between system resources and tracking capabilities is analyzed quantitatively; compared with the array element number, the influence on the system performance by the transmitted power becomes obvious and the influence on the target tracking precision and number by the system resources is displayed, so that the computing load of the system is reduced, the good system performance is realized, and the overall multi-target tracking accuracy is improved. The joint resource optimization method has the great practical application value.

The invention discloses a CS-Jerk model-based parameter adaptive maneuvering target tracking algorithm. According to the algorithm, on the basis of a CS-Jerk model and current statistics thoughts, truncation probability distribution is utilized to describe current probability density of the acceleration change rate of a target, so that the relation of the variance of the acceleration change rate of the target and the Jerk mean is obtained, and therefore, the adaptive adjustment of the variance of the acceleration change rate of the target is realized; the change of the maneuvering condition of the target is judged through using residual vectors; and the adaptive adjustment of maneuvering frequency is realized through a nonlinear maneuvering frequency function; and the adaptive adjustment of a process covariance matrix Q (k) is realized, and the problem that a CS-Jerk model needs the manual setting of a process covariance matrix can be solved, and target tracking performance can be improved.

The invention discloses a multi-camera multi-target tracking method. The multi-camera multi-target tracking method comprises the steps of collecting an initial track fragment set collected by multiplecameras; carrying out equalization processing on the trajectory fragment similarity distribution of the initial trajectory fragment set to obtain a pre-processed trajectory fragment set; and based onthe pre-processed track segment set, fusing each pre-processed track segment to obtain a complete track of each target, and completing the multi-camera multi-target tracking. According to the invention, by adopting a one-step walking idea, the track segments under the plurality of cameras are put together for track fusion; moreover, when all the tracks are fused, the equalization processing is carried out on the similarity distribution of all the track segments, so that the similarity of all the track segments corresponding to the single target is ensured to be at the same level, the efficiency and the effectiveness of multi-camera multi-target tracking are improved, and the problem that the accumulated errors exist when the single camera track segment fusion is firstly carried out and then the cross-camera track fusion is carried out, is solved to a certain extent.

The invention provides a slow moving target elimination method based on a point clutter map. The method is used for improving tracking precision of oversea low-altitude flight target, and comprises the following implementation steps: calculating a Doppler-distance dimension data matrix Dj on an initial orientation alpha received by a radar in each frame j; calculating a test statistics matrix lambda j of the Doppler-distance dimension data matrix Dj; performing Doppler condensation on the test statistics matrix lambda j; acquiring an initial test result Hj; extracting a slow Doppler channel index j set M'; obtaining the test statistics matrix (represented as a formula in the specification) of a slow Doppler channel; getting rid of the slow moving target in the test statistics matrix (represented as a formula in the specification) of each frame j; and getting rid of the slow moving targets in pulse-distance dimension data in the other orientations, except for the orientation alpha, in all the frames. With the method provided by the invention, performance of eliminating the slow moving target in a low speed channel is improved, and the method can be used for elimination of sea surface slow moving targets uninterested under a radar on sea mode.

The invention discloses a real-time tracking method for an infrared image target with multi-feature fusion. The real-time tracking method comprises the following steps of: initializing a target tracking point position; initializing a target model; calculating a target candidate model; calculating a coefficient of unified feature Bhattacharyya and weight coefficients between feature; calculating a new tracking position of a current frame target; estimating the coefficient of the unified feature Bhattacharyya at the new position; and comparing the two coefficients of the unified feature Bhattacharyya and outputting results. The real-time tracking method can be used for adaptively calculating the weight coefficients between multiple features, enhances the robustness of target tracking, ensures the stability of target tracking, solves the problem of tracking point drifting caused by unstable single feature, and effectively improves the accuracy of target tracking.

The invention provides a distributed type target tracking method of a mobile wireless sensor network, which solves the problem of low tracking accuracy in the target tracking of a traditional mobile wireless sensor network. The distributed type target tracking method comprises the following steps: a distributed type algorithm is used for predicting the position of a target in next tracking period, the cooperative matching of static nodes and dynamic nodes in a mobile wireless sensor network is considered, and all mobile nodes move towards the position of the target according to the information and acquire more accurate information of the target. The distributed type target tracking method provided by the invention has the advantages that the accuracy of target tracking of the mobile wireless sensor network can be effectively improved, the dynamic connectivity of the network is ensured, the target tracking efficiency is improved, the target tracking period is shortened, and the target tracking execution process is accelerated.

The invention relates to a quick real-time discrimination type tracing method based on multi-local-feature learning. The method comprises the following steps of: single-frame decomposition is carried out on a video, a target to be traced is marked in an initial frame through combination with early-stage action identification work or an artificial marking method, then, candidate blocks are obtained in a candidate area through dense sampling, and the local feature of each candidate block is independently calculated; then, on the basis of a circulant matrix and a relevant filter, a classifier is trained in a Fourier domain, a detection formula is used for calculating correlation between each candidate block in a current frame and the target also in the Fourier domain in a position detection stage, and an area with the highest correlation is selected as a prediction position of the target of the current frame; and finally, in the prediction position, a position detection result which has a smallest difference with a previous frame of target is selected as a final tracing result of the current frame, and a new target feature is used for training a new classifier. Except the initial frame, the tracing of other frames is characterized in that position detection is firstly carried out, and then, the training of a new regression function is carried out.

The invention belongs to the field of radar target tracking, and particularly relates to a spatial-temporal resource-waveform selection management method for a multi-beam centralized MIMO radar. The spatial-temporal resource-waveform selection management method comprehensively considers target detection and target tracking, and achieves efficient allocation of system resources in tracking processwhile improving target tracking accuracy. When solving a related algorithm, the proposed algorithm first selects combinations of a feasible sub-array division, a sampling periods, emission energy, anirradiated target set and sub-array beam pointing set parameters through a target successful irradiation limit and a target effective detection probability limit to adaptively allocate radar system resources; then further improves tracking accuracy, combines transmitted waveform parameters to form combinations of the feasible sub-array division, the sampling period, the emission energy, the irradiated target set and the sub-array beam pointing set parameters and the transmitted waveform parameters, and finally selects an optimal combination of the sub-array division, the sampling period, the irradiated target set and the sub-array beam pointing set parameters and the transmitted waveform parameters according to principle of target function minimization.