118results about How to "Achieve goal tracking" patented technology

The invention relates to the field of unmanned aerial vehicle target tracking and image processing, and discloses an unmanned aerial vehicle target tracking system based on machine vision and an ultra-wideband positioning technology. The target tracking system is composed of a ground control platform S1 and an unmanned aerial vehicle tracker S2. The ground control platform is composed of a piece of flight control software, a data transmission module, and a video display interface. The unmanned aerial vehicle tracker is composed of a microprocessor, an FPGA, a positioning module, an airborne sensor, an intelligent vision module, a flight control module, and a data transmission module. The ground control platform sends a target tracking command. After receiving the target tracking command, the unmanned aerial vehicle tracker performs algorithm processing on an image acquired by the intelligent vision module and automatically identifies the position of a target in the image, and meanwhile, the unmanned aerial vehicle tracker reads data of the positioning module and the airborne sensor, plans a flight route according to a gesture guiding and adjusting algorithm, and sends a target moving image to the ground control platform to realize automatic visual tracking of the moving target.

The invention discloses a vehicle detection method based on laser and vision fusion. The method comprises the following steps of 1) acquiring target detection information for an input image and laserpoint cloud; 2) performing optimal matching on the images of the front frame and the rear frame and the point cloud detection frame, and establishing a tracking sequence of an image and point cloud detection target; 3) fusing the tracking sequences of the image and the detection frame thereof and the tracking sequences of the point cloud and the detection frame thereof; 4) classifying all the target detection boxes, outputting a fusion list, and outputting a fusion result; and 5) obtaining the accurate position of the surrounding vehicle relative to the vehicle in the current frame, reading the next frame of image and the point cloud data, circulating the steps 1) to 5), and outputting a fusion detection result. According to the method, on the basis of point cloud and image target detection, the detection result is subjected to information tracking, the detection result is optimally matched, and the fusion result is preferentially input into the final fusion list, so that compared witha single sensor target detection method, the target detection precision is improved, and the false detection rate is reduced.

The invention discloses a heterogeneous unmanned aerial vehicle cluster target tracking system and method based on biosocial force. The target tracking system includes a cluster hardware system architecture, a cluster software system architecture, a cluster communication system architecture and a cluster flight management and control system. The method comprises the following steps that 1, modeling of an unmanned aerial vehicle state is carried out; 2, a consistency protocol is designed; 3, unmanned aerial vehicle control instructions are designed based on a phase difference consistency algorithm; 4, trajectory tracking based on non-linear guidance is carried out; 5, a heterogeneous unmanned aerial vehicle cluster system based on the biosocial force is described; and 6, heterogeneous unmanned aerial vehicle cluster target tracking based on the biosocial force is carried out. The heterogeneous unmanned aerial vehicle cluster target tracking system and method based on the biosocial forcehas the advantages that the heterogeneous unmanned aerial vehicle cluster target tracking is realized, the speed of controlling an unmanned aerial vehicle to gradually converge to an expected path isincreased, and the system of the whole target tracking system is complete, the functions are perfect, and the verification of targets in different task scenes can be realized.

The invention discloses a moving object relay tracing algorithm of multiple PTZ (pan/tilt/zoom) cameras. The moving object relay tracing algorithm is characterized in that the moving object relay tracing algorithm is carried out according to the following steps: 1. estimating inner parameter matrixes of the PTZ cameras by adopting a camera self-calibration method; 2. setting a view cutting line between adjacent PTZ cameras; 3. using Logistics regression model as a classification function, and combining with a mean value drifting algorithm and realizing the target tracking; 4. continuously adjusting angles of the PTZ cameras in the course of tracking, so that the target is always located in the center zone of the PTZ camera view; 5. when the target exceeds the view cutting line of the PTZ camera at present and enters the monitoring view of the adjacent PTZ camera, calculating the coordinate of the target in the adjacent PTZ camera view, and transferring the adjacent PTZ cameras to track the target, and rotating the original PTZ camera to return the preset position. The moving object relay tracing algorithm can accurately control the camera rotation, and perform stable tracking for a long time on the target; and thereby, the complete historical movement information of the target can be obtained.

The invention discloses an unmanned aerial vehicle target tracking method, which comprises the following steps: (1) obtaining a left-eye image and a right-eye image by using a binocular camera image acquisition device; (2) calculating the position coordinates of the output UAV in the world coordinate system by the visual SLAM algorithm; (3) detecting the target in the panoramic image composed of the left-eye and the right-eye images, and calculating the three-dimensional coordinates of the output tracking target in the camera coordinate system; (4) taking the real-time position of the UAV in the world coordinate system and the real-time position of the tracking target in the camera coordinate system as the input of the speed control algorithm, and calculating the desired speed as the inputof the flight controller of the UAV to control the flight parameters of the UAV and realizing the target tracking. The method performs autonomous tracking through visual positioning while detecting targets, and is particularly suitable for tracking tasks in weak GPS or GPS-free environments.

The invention discloses a target tracking method based on triple twin hash network learning, and relates to the technical field of computer vision, target tracking and deep learning. According to themethod, firstly, a triple twin hash network is constructed, and the network is composed of a data input layer, a convolution feature extraction layer and a hash coding layer. In the initial training process of the network, a training data set and a random gradient descent back propagation algorithm are used for training the triple twinning Hash network, and after training is completed, the initialcapacity of target positioning can be obtained through the network. In the tracking process, firstly, an input image passes through a triple twin region recommendation network to obtain correspondingcandidate frames, then the candidate frames are input into a triple twin Hash network to be subjected to forward processing, the similarity between each candidate frame and a query sample is calculated, the candidate frame with the highest similarity is selected as a tracking target object, and therefore target tracking is achieved.

The invention relates to an object tracking method for an intensive scene based on multi-module sparse projection. The object characteristic is characterized by a sparse projection method; and for solving the serious mutual shield problem in an intensive scene, a reconstruction matrix based on a multi-module core color histogram is designed and further a corresponding object matching and updating algorithm is designed. According to the object tracking method, the automatic tracking of objects in the people stream intensive scene in a public place is realized; and meanwhile, the solution to the problem of serious mutual shield in the intensive scene is provided.

The invention provides an anti-unmanned aerial vehicle detection tracking interference system and a photoelectric tracking system working method. The anti-unmanned aerial vehicle detection tracking interference system comprises a radar, a photoelectric tracking system, an unmanned aerial vehicle interference unit and a holder; the photoelectric tracking system comprises a motion detection module,a correlation filtering target tracking module, a deep learning target detection module and a deep learning target tracking module; the radar is in communication connection with the photoelectric tracking system; and the photoelectric tracking system is in communication connection with the holder. According to the anti-unmanned aerial vehicle detection tracking interference system, when target distance is farther, the deep learning target detection module cannot extract target characteristics, and target detection is carried out by using the motion detection module; when the target distance isfarther, under the condition that the deep learning target tracking module cannot extract target characteristics, target tracking is carried out by using the correlation filtering target tracking module; and the problem that the deep learning target tracking module cannot provide degree of confidence is solved by using data of the correlation filtering target tracking module.

The invention discloses a target tracking system based on the cooperation of an UAV and an unmanned vehicle. The target tracking system includes an UAV, a workstation and an unmanned vehicle, whereinthe UAV includes a camera, an image transmission module and a control signal receiver. The camera transmits a captured image with position and feature information to a ground signal receiver via the image transmission module. After receiving an image captured by the camera on the UAV, the workstation searches and accurately positions a target object and an obstacle. A signal transmission module transmits the position information of the target object and the obstacle to the onboard computer of the unmanned vehicle. The onboard computer designs the driving route of the unmanned vehicle accordingto the position information of the target object and obstacle, and controls the vehicle to advance to the target object to complete the target tracking. By means of the cooperation of the UAV and theunmanned vehicle, the target tracking system improves the reliability, the accuracy, and the work efficiency of target tracking.

The invention relates to a target tracking method. The target tracking method comprises the steps of performing target detection at a static state of equipment; when a target is detected to appear in a monitoring picture, performing positioning on the target; predicting a moving direction of the target according to a movement track generated by the target; controlling the equipment to rotate according to the moving direction of the target when the target is detected to have a trend of leaving the field of view of a camera; and tracking the target by adopting a dynamic background modeling mode at a rotating state of the equipment. According to the invention, the amount of calculation is reduced, thus the hardware cost can be saved, and continuous tracking for the target under rotation of the monitoring equipment is realized.

The invention discloses a small low-flight target visual detection tracking system and a method thereof. The system comprises a video data input unit, a video preprocessing unit, a training data construction unit, a detection model training unit, a target comparison screening unit, a detection correction unit, a reference frame initialization unit, a sample library dynamic construction unit, an online learning unit, a position refinement unit, a decision control unit and a tracking result output unit. The method comprises the following steps: constructing a target detection network, and comparing and screening targets; carrying out target tracking online learning; and dynamically constructing a classifier training sample library, and performing target tracking position refinement. The system and the method have the following advantages: the tracking drift condition of the tracking target caused by factors such as shielding, scale change and illumination can be effectively relieved, androbust target tracking can be realized; the capability of updating reference frame features in time according to target changes is achieved; and meanwhile, error tracking caused by updating the reference frame features can be avoided by introducing a feature point matching algorithm.

The invention discloses an extended target tracking method based on variational Bayesian expectation maximization (VBEM) and mainly solves the problem that tracking performance of a target is weakened dramatically under the condition that measurement noise covariance is unknown in the conventional extended target tracking field. The extended target tracking method includes firstly predicting relevant parameters of Gaussian inverse gamma components in joint probability hypothesis density of a target state and the measurement noise covariance; updating the parameters of the Gaussian inverse gamma components; finally acquiring the extended target state and the number by construction and combination. It is proved by simulation experiment that multiple extended targets can be well tracked under the unknown number and the unknown measurement noise covariance, and the extended target tracking method is high in tracking accuracy and can be used for tracking aircrafts and submarine targets.

The invention relates to a visual identification system applied to an unmanned aerial vehicle for target tracking and locking. The system comprises a ground control platform and an unmanned aerial vehicle tracker, wherein the ground control platform comprises flight control software, a data transmission module and a video display interface; and the unmanned aerial vehicle tracker comprises a microprocessor, an FPGA, a positioning module, an airborne sensor, an intelligent vision module, a flight control module and a data transmission module. According to the system, a target tracking instruction is sent through the ground control platform, the unmanned aerial vehicle tracker automatically identifies the position of a target in an image coordinate system of images collected by the intelligent vision module by the adoption of an HOG characteristic and SVM combined rapid target detection algorithm after receiving the target tracking instruction, data of the positioning module and the airborne sensor is read, target moving images are issued to the ground control platform, and therefore automatic visual tracking of the moving target is realized. The system is high in automatic degree, easy to operate, high in precision and excellent in real-time tracking performance, and spatial coordinates are digitalized.

The invention relates to a target tracking method under arbitrary linear constraints, comprising: acquiring target position measurement information from an observation radar to construct a measurementvector, wherein the measurement vector comprises distance measurement and azimuth measurement of a target relative to the original of an observation radar coordinate system; performing state augmentation of the state vector of the target at the current time point using the state vector of the target at previous time points to obtain an augmented state vector and its corresponding state equation,wherein the augmented state includes states of the k time point and the previous d consecutive time points, and d represents the time span of the augmented part; constructing pseudo-measurement according to the linear trajectory shape of the target motion to describe the arbitrary linear constraint relationship, and augmenting the pseudo-measurement into the measurement vector to obtain an augmented measurement equation; and using a nonlinear filtering method, and performing filtering by using the augmented state equation and the augmented measurement equation. The method constructs the pseudo-measurement, uses the linear trajectory shape information to improve the filtering precision, and provides a new solution for the target tracking problem under any linear constraints.

The invention discloses an aerial photographing method, device and an unmanned aerial vehicle. The method comprises the following steps: acquiring images through two cameras; splicing the two images acquired by the two cameras into one 3D image; and sending the 3D image out. Thus, the unmanned aerial vehicle can provide the 3D image in the process of aerial photographing, and users are allowed to view the 3D image with the lifelike picture in real time, thereby bringing the immersive experience to the users, and greatly enhancing the aerial photographing experience of the users. Further, when the 3D image is transmitted to the users for viewing in real time, depth detection is carried out through the 3D image, so that the unmanned aerial vehicle can realize multiple functions, such as 3D aerial photographing, obstacle avoidance, tracking, ranging and so on by using a group of binocular cameras (that is two cameras), thus 3D photographing and depth detection can be respectively realized without respectively using the two groups of binocular cameras (that is four cameras), and multiple functions can be achieved at a lower cost.

The invention discloses an object tracking method based on simplex method, comprising the following steps of: selecting an object from an obtained image to carry out tracking processing; through the steps of target selection, image imputing, simplex peak selection, calculation of distance between the peak and the target, peak sequencing, simplex operation, target predication, and the like, finding out the node with the minimum distance to the target after certain times of iteration, that is, the target is at the position of the current image, so that the tracking for the target object is realized. The method can realize real-time and quick tracking of moving objects, and can automatically recover after the tracking is failed.

The invention relates to the technical field of flight control, in particular to a target tracking method and device and an aircraft. The method includes the steps of receiving GPS information of a tracking target, calculating a first speed and a first direction of the tracking target according to the GPS information, estimating a target position of the tracking target at the next time point according to the first speed and the first direction, obtaining a second speed and a second direction of the aircraft according to the first speed and the first direction, and controlling the aircraft to fly to the target position according to the second speed and the second direction to achieve target tracking. The target tracking method has a good tracking effect.

The invention provides a real-time target tracking system and method in an airborne photoelectric platform, and belongs to the technical field of intelligent video processing. The system comprises animage collecting module, an image decoding module, a data communication module and a target tracking module. The image collecting module is responsible for collecting visible light image data and infrared image data and inputting the infrared image data and the visible light image data into an image caching module after collection is completed. The data communication module is responsible for transmission of instruction information and image information between the data communication module and a DSP chip and the communication between an upper computer and a rear end servo control system. Thetarget tracking module receives instructions from the upper computer, reads the cached infrared image data and visible light image data, outputs a corresponding processing result (target direction information), and finally transmitting the target position information to the rear end servo control system. The system is easy to obtain, can instantly conduct real-time target tracking processing in the airborne photoelectric platform after images are collected, is high in precision and stability and low in output delay, and can replace manual work for implementing the intelligent control over a servo system.