696 results about "Track algorithm" patented technology

The present invention relates to an adaptive sensing-based lightweight monitoring method for a fine crack in a complex background region. The method comprises the following steps: step S1, on the basis of region division, performing automatic acquisition of crack information, wherein PTZ camera sensors are used to automatically perform block-wise acquisition on crack regions; step S2, performing an adaptive complex scale calibration process, using a multi-scale template matching algorithm to adaptively correct distortion information of all regions, and performing real-scale conversion from pixel precision; step S3, constructing a lightweight crack segmentation network to process data processed in step S2; and step S4, by means of a quantitative crack-tracking algorithm based on Euclidean distance similarity classification, performing real-time monitoring on each piece of crack dynamic information. Compared with the prior art, the present invention has advantages such as achieving efficient, accurate, and online monitoring and analysis of cracks.

The invention relates to the technical field of marine monitoring, in particular to a marine target visual servo tracking method, which comprises the steps of multi-modal sensor fusion, a self-adaptive visual tracking algorithm, a servo control and visual collaboration mechanism and a shielding processing and target re-identification strategy. The problem that tracking is unstable under the conditions of illumination change, ship body shaking, target shielding and the like in a traditional method is solved. The IMU, the GNSS and the visual data are fused through extended Kalman filtering, ship body shaking is compensated, and the target state estimation precision is improved; the improved D-Fi ne target detection model is combined with an online feature updating mechanism to dynamically adapt to the appearance change of the target; the prediction and correction control strategy and the double-closed-loop PI D controller cooperate to adjust the camera holder, and the tracking delay is reduced; the multi-clue shielding detection and space-time joint feature matching technology ensures accurate re-identification of the target after shielding is removed. The real-time performance and robustness of the tracking system on an embedded platform are improved, and an efficient and stable target tracking solution is provided.

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention discloses a millimeter wave radar human body tumble detection method based on multi-feature fusion, and the method comprises the steps: obtaining an original point cloud frame from each radar, and carrying out the timestamp calibration and space coordinate system conversion; performing noise filtering, ground segmentation and human body point cloud extraction on each frame of point cloud; dividing the preprocessed point cloud into a static cluster and a dynamic cluster; filtering false dynamic clusters; extracting a residual dynamic cluster set, and identifying and tracking the human body dynamic clusters in continuous frames by adopting a tracking algorithm; extracting a corrected time sequence feature from the tracked human body cluster; inputting the time sequence characteristics into a pre-trained deep time sequence network, learning a falling time sequence dependency relationship, and outputting an abnormal index; and performing multi-source fusion with the abnormal index to obtain a comprehensive index to judge whether to trigger an alarm. The method can adapt to a complex home environment, improves the detection accuracy and real-time performance, and reduces the false alarm and missing alarm.

The invention provides a multi-target tracking method based on a YOLOv8 model and a Byte Track algorithm, and relates to the technical field of computer vision and edge equipment. The method specifically comprises the following steps: acquiring data of a plurality of images, performing format conversion, and constructing an image data set; constructing a DC-YOLOv8 network structure, and performing training by using the image data set to obtain a target detection model based on the DC-YOLOv8 network structure; obtaining a to-be-detected video stream, extracting continuous image frames from the to-be-detected video stream, and preprocessing the extracted image frames; and inputting the preprocessed image frames into the target detection model for target detection, and performing target tracking on all detected targets by adopting a Byte Track algorithm to obtain tracking trajectories of the tracked targets in all the image frames and generate a video stream. According to the invention, target tracking can be carried out on a video with many targets more smoothly.

The invention relates to the technical field of intelligent unmanned sweeping, in particular to an unmanned sweeping vehicle edge path tracking control method and system. Through cooperative work of a path planning and edge feature detection module, a real-time positioning and attitude estimation module, an edge tracking control module, an obstacle detection and avoidance module, a path correction and feedback module and an energy consumption optimization and endurance control module, precise tracking of the sweeper on an edge path in a complex environment is realized. Through multi-sensor data fusion, extended Kalman filtering and a self-adaptive path tracking algorithm, the system can adjust the driving state of the motor sweeper in real time, it is ensured that the motor sweeper is driven accurately along the edge, through a dynamic path planning algorithm and an energy consumption optimization model, the ability of the motor sweeper in the aspects of obstacle avoidance, path correction and endurance control is effectively improved, and the motor sweeper driving stability is improved. Therefore, the working efficiency and the endurance time are improved on the premise that the cleaning task requirement is met. The device is suitable for various complex sweeping environments, and efficient edge sweeping operation is achieved.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

The invention relates to the technical field of target tracking, in particular to a photoelectric tracking algorithm and system for self-adaptive target tracking, and the system comprises an image collection module, a cross-spectrum self-adaptive sensing module, an image processing and target recognition module, a self-adaptive tracking algorithm module and a servo control module, and constructs a closed-loop feedback link to achieve the synchronous optimization of parameters in a whole link. The algorithm comprises the steps of image acquisition and adjustment, target detection, parameter optimization, servo driving and feedback. The system can accurately estimate a high-speed turning target, is adaptive to complex environments such as low illumination / haze and the like, completes sheltered target recapture within 0.5 s, has servo compensation precision of + / -0.1 degree, is suitable for airport bird monitoring, port unmanned aerial vehicle tracking and highway vehicle monitoring scenes, and significantly improves tracking stability and practical value.

The invention discloses a high-precision continuous tracking method for a strong-maneuvering infrared weak and small target under a space-based detection visual angle, and the method comprises the steps: obtaining the uncertainty measurement of a target at a non-maximum suppression stage of a detection result, and enabling the uncertainty measurement to act on a target state updating and data association stage; therefore, the experience distribution of the detector is fully transmitted to the tracking process, and the tracking accuracy is improved. In order to cope with a complex target maneuvering state, an interactive multi-model technical route is adopted to design a tracking algorithm; in order to reduce the dependence on a prior motion model, a dynamic Markov transfer matrix construction method is designed, and the model transfer probability is updated in a mode of comprehensively modulating historical dynamic information and current static information; in a data association stage, targets with different scales are associated by integrating advantages of IoU and NWD, uncertainty is transmitted to a cost calculation process, and tracks and targets which are indefinitely matched are processed based on scale invariant and energy invariant hypotheses, so that high-precision continuous tracking of the targets is realized.

The invention relates to the technical field of fault equipment detection, and discloses a power equipment fault detection method and system based on high-precision temperature measurement, and the method comprises the steps: scanning the surface temperature of power equipment through a high-precision temperature measurement device, obtaining a temperature distribution image, processing the temperature distribution image, and obtaining a temperature distribution matrix; carrying out gradient analysis and temperature difference identification on the temperature distribution matrix to obtain a hot spot candidate area; performing clustering analysis on the hot spot candidate region to obtain an initial hot spot region, and generating a static boundary by adopting a boundary tracking algorithm; correcting the static boundary according to the temperature change of each boundary point to obtain a hot spot area; and performing fault risk assessment according to the load data of the power equipment and the temperature data of the hot spot area. According to the invention, through temperature data analysis and region boundary correction, the hot spot region can be accurately identified, and in combination with a machine learning algorithm, the accuracy of power equipment fault risk assessment is improved, and safe and stable operation of a power system is ensured.

The invention relates to the technical field of director and picture synthesis methods, in particular to an intelligent director and picture synthesis method of a panoramic live broadcast system, which comprises the following steps: acquiring panoramic video data shot by a panoramic camera; obtaining user feedback data; based on the panoramic video data, generating a shot selection strategy by using a deep reinforcement learning algorithm; detecting a live broadcast key event in real time by using a target detection and tracking algorithm based on the panoramic video data; generating a picture shooting plan sequence according to the shot selection strategy and the live broadcast key event; performing intelligent editing and synthesis on the panoramic video data based on the picture shooting plan sequence; and outputting the synthesized live video. And updating parameters of the deep reinforcement learning algorithm based on the user feedback data, thereby realizing full automation of the program directing process, greatly improving the efficiency, and enabling the system to continuously learn from live broadcast experience through the deep reinforcement learning algorithm and gradually improve the decision-making ability of the system.

The invention discloses a pedestrian multi-target detection and tracking algorithm based on cross-layer fusion and dynamic adjustment, and aims at the problem that a deep convolutional neural network in a backbone network is frequently excessively parameterized, a composite scaling mechanism is introduced to reduce the parameter quantity and improve the feature extraction capability of the network; meanwhile, considering that targets with different scales exist in a data set, a lightweight cross-scale feature fusion module is fused at the neck of the network, so that the adaptability of the model to scale change is enhanced; besides, aiming at the problems of small targets and shielding targets, a new loss function InnerWiseWIoU is designed, and in combination with internal region optimization and context information, the target positioning precision is improved. According to the method, the new model is applied to pedestrian tracking, the shielding degree between pedestrians is calculated and the matching threshold is dynamically adjusted aiming at the condition that a fixed confidence threshold in a tracking task is not suitable for centralized and rapid change of a target, so that the tracking performance is effectively improved, and the adaptability in a complex scene is improved.

The invention relates to a tracking navigation method for positioning a crop center point based on laser radar and image fusion, and belongs to the technical field of navigation, and the method comprises the steps: carrying out the marking of a farmland image after distortion correction, generating a training sample, inputting the training sample into a U-net model, carrying out the training, obtaining a crop segmentation model, and carrying out the classification of crops and weeds; performing feature fusion on the filtered laser radar point cloud data and the farmland image after distortion correction to obtain crop point cloud with semantic information; calculating a crop center point according to the crop point cloud with the semantic information; and providing navigation for the agricultural robot by taking the crop center point as a preview point. The spatial structure information of the target crop is obtained through the laser radar and the camera, the semantic point cloud is generated through feature fusion, the crop center point is dynamically extracted from the point cloud to serve as a real-time preview target, a fixed preview distance mechanism in a tracking algorithm is replaced, centimeter-level precise navigation is achieved, and the precision of the target crop is improved. And the operation precision and the operation safety of the agricultural robot are greatly improved.

A video file may be presented via a user application that displays one or more video frames of the video file. A user request to perform an object detection for objects of a specific object type in a video frame of the video file may be received from the user application. A machine-learning model of a plurality of machine-learning models that is configured to detect objects of the specific object type may be applied to the video frame to detect an object of the specific object type in the video frame. Each of the plurality of machine-learning models may be trained to detect objects of a corresponding object type. Subsequently, an object tracking algorithm may be applied to one or more additional video frames of the video file to track the object of the specific object type across the one or more additional video frames.

The invention provides a tunnel congestion node real-time estimation method and system based on digital twinning, and the construction of a digital twinning system physical scene of a tunnel is realized based on a Unity engine and SUMO software by determining the spatial characteristics, traffic information, camera information and the like of the tunnel. And then, dynamic and static data information of the vehicle is obtained through a CornerNet image detection network and a Deepsort tracking algorithm, the driving condition of the lost vehicle is deduced and simulated according to an acceleration distance control car-following model and a safe distance lane changing model, and dynamic target projection and full tracking of the digital twin system are realized based on an improved Hungary algorithm. And finally, node division of vehicles in the tunnel is realized according to the proposed intelligent traffic flow node model, node saturation is introduced, a node coupling congestion identification model is proposed, and tunnel congestion event sensing and congestion node positioning are realized. According to the invention, on one hand, traffic management resumption in the tunnel can be carried out by means of the digital twin system, and the congestion process and reasons are deeply analyzed; and on the other hand, the traffic flow congestion condition in the tunnel can be detected in real time, and the congestion node can be determined, so that the congestion traffic flow can be dredged in advance, and the accident occurrence probability in the tunnel can be reduced. And the method is of great significance to future digital and intelligent traffic flow research.

The invention relates to a target tracking system based on multi-modal deep feature proxy cross attention guidance, and the method combines and uses a visible light image and a thermal infrared image, and improves the performance of a tracking algorithm under a complex illumination condition through an innovative proxy cross attention mechanism. The system comprises a multi-modal detector and a data associator, wherein the detector consists of a double-branch feature extraction network, a proxy cross attention feature enhancement module and a feature pyramid sharing convolution module; the data correlator comprises a neural Kalman filter and a low-confidence-coefficient-based detection box reuse strategy, the challenge of a complex motion scene is solved by dynamically adjusting process noise and observation noise parameters, and the long-term tracking performance is improved. According to the method, multi-modal data fusion is carried out on a deep feature level, so that target detection and tracking are optimized, and the tracking precision and stability in a complex environment are improved.

The invention provides a three-dimensional tracking method applied to a single-photon laser radar to detect a far-field dynamic unmanned aerial vehicle point target, and belongs to the technical field of laser radar detection and target tracking. The problems that in the prior art, under the long-distance condition, the target imaging size is smaller than one pixel, and a detection and tracking algorithm based on shape, texture or edge features fails are solved. The method comprises the following steps: constructing a six-dimensional state vector containing a three-dimensional position and a three-dimensional speed of a target; predicting a state vector according to a state transfer function containing air resistance and turning acceleration; and an extended Kalman filtering framework is utilized to update the predicted state and obtain state estimation of the target, and the extended Kalman filtering framework comprises a self-adaptive process noise adjustment mechanism based on innovation feedback and is used for adjusting a process noise covariance matrix on line. The method is mainly used in the field of detection and multi-dimensional tracking of airspace moving targets.

The invention discloses an optical imaging zoom point selection correction method and system, which can correct the deviation between a theoretical model and actual lens characteristics in real time through a peak point tracking algorithm and offset vector calculation in a second stage, effectively solve the problem of individualized errors caused by lens group assembly tolerance, material refractive index difference and the like, and improve the correction accuracy. The stratified sampling strategy of the first stage is combined with the slope interpolation of the third stage, untested points in a theoretical curve are filled, a high-precision focusing curve is generated, the full range of zooming is covered, and local focusing failure caused by traditional uniform sampling is avoided; according to the method, curve jitter caused by test noise or mechanical vibration is eliminated, a smooth zoom focusing curve is output, the anti-interference capability in actual control is improved, frequent jitter of a focusing motor is avoided, a search strategy is dynamically adjusted in combination with definition data, an actual peak point is quickly locked, and the problem of overmodulation or slow convergence caused by a traditional fixed step length is solved.

The invention discloses a cigarette hard carton damage defect detection system and method, and the system comprises an image collection unit which comprises a shooting angle adjustable camera shooting assembly and an array type LED light source assembly; the image processing unit is used for carrying out gray processing, edge detection, dynamic binarization, denoising, sharpening enhancement and the like on the acquired image, and the image detection unit is used for generating a dynamic mask area to carry out defect detection on the image based on a detected straight line and dynamic rectangular tracking algorithm. Performing spatial positioning on spots in the image based on a rectangular matching algorithm of coordinate transformation, and when detecting that a certain spot is not in any rectangular range, judging whether the spot is a defect or not by combining a grading algorithm based on a color block area threshold value; and the result execution unit is used for outputting the defect detection result to the elimination mechanism, the early warning mechanism and the display assembly. According to the invention, the embedded visual detection module is used for carrying out machine visual online detection on the damage and scratch defects of the carton, so that the sound-light alarm and elimination of the defective cigarette carton are realized, the defective cigarette carton is prevented from entering the next process, the long-time tracing caused by difficulty in finding problems is also avoided, and the production efficiency is improved. And the control capability of a workshop aiming at the carton defect quality problem is greatly improved.

The invention provides a power grid frequency detection method, system and device and a medium, and the method comprises the steps: carrying out the narrowband signal decomposition of an obtained input voltage signal of a power grid through a null-space tracking algorithm, and obtaining a fundamental main mode component of the input voltage signal; performing low-pass processing on the fundamental wave main mode component to obtain a fundamental wave signal of the fundamental wave main mode component; performing discrete Fourier transform on the fundamental wave signal to obtain phase information of the fundamental wave signal; according to the phase information of the fundamental wave signal, calculating phase deviation information of the fundamental wave signal in pre-selected adjacent cyclic waves to obtain frequency estimation information of the power grid; according to the invention, narrow-band decomposition is carried out on an input voltage signal through a null-space tracking algorithm, so that a fundamental main mode can be accurately extracted; and phase deviation information can be determined based on discrete Fourier transform, so that when the interference frequency of the power grid is close to the fundamental frequency, high separation precision can be kept, and the accuracy of power grid frequency estimation can be improved.

The invention provides a water column detection tracking algorithm based on a ByteTrack multi-target tracker. The algorithm comprises the following steps: step 1, obtaining video frame data of a to-be-detected object; step 2, inputting the video frame data into a static feature extraction network, performing feature extraction and feature fusion on the video frame data, and outputting a detection frame corresponding to each detected water column target and a corresponding confidence coefficient; step 3, predicting the track of the previous video frame through a Kalman filtering model to obtain a prediction track of the current frame, and outputting a prediction frame corresponding to the prediction track; and step 4, through a Hungary algorithm, based on the confidence coefficient, matching the detection frame and the prediction frame, and outputting a successfully matched water column target trajectory and a unsuccessfully matched water column target trajectory. The method can comprehensively consider the static characteristics and the dynamic characteristics of the water column, thereby providing an accurate water column detection tracking result, and providing technical support for an offshore drop point precision evaluation task.

The invention discloses a V2V-based intelligent networked automobile road adhesion coefficient estimation method and system, and relates to the technical field of intelligent networked automobiles. The method comprises the steps that physical parameters and motion state information of a front vehicle are received based on V2V, an event triggering communication mechanism is adopted in the sending process of the physical parameters and the motion state information of the front vehicle, and meanwhile data packet loss is considered; constructing a three-degree-of-freedom vehicle dynamics model of the front vehicle; and establishing a road adhesion coefficient estimator based on an event triggering strong tracking unscented Kalman filter considering packet loss. According to the method, the front vehicle is regarded as a sensor of the vehicle, the parameters and motion information of the front vehicle are obtained through vehicle-mounted wireless communication, the problems of data packet loss and limited communication bandwidth during communication are considered, the robustness of an estimator to model parameter perturbation is improved through a strong tracking algorithm, and the estimation accuracy is improved. And finally, establishing a road adhesion coefficient estimator based on an event triggering strong tracking unscented Kalman filter considering packet loss, and ensuring that the road adhesion coefficient is accurately obtained in advance.

The invention relates to a sight angle rate extraction method based on depth target tracking, and relates to the field of navigation control and computer vision. Comprising the steps of deploying a variable-focus optical camera on an unmanned aerial vehicle, and setting a normal of the center of a mirror surface of the camera to be inclined at a certain angle with a horizontal plane for acquiring complete information of a target; calibrating the target in the camera based on an optical parameter calibration method, and obtaining a field angle of the target relative to the center of the camera; deploying a target tracking algorithm on the unmanned aerial vehicle, and identifying a target position based on the target tracking algorithm; obtaining a horizontal deflection angle and a pitching deflection angle of the target relative to the center of the camera by combining a target position identified by a target tracking algorithm based on the field angle; and estimating the line-of-sight angular rate through Kalman filtering based on the obtained horizontal deflection angle, the obtained pitching deflection angle and the flight parameters of the unmanned aerial vehicle. Through combination of parameter calibration of the optical camera and a target tracking algorithm based on deep learning, the horizontal deflection angle and the pitching deflection angle of the target can be accurately obtained.

The present invention relates to a targeted sensing-oriented twin substructure interaction method and system and the use. The method comprises: acquiring multi-element inspection and monitoring data and finite element influence line data of a main structure; on the basis of the inspection and monitoring data and the finite element influence line data of a non-key area, using a sparsity adaptive matching pursuit algorithm to solve a boundary condition of a key area, so as to achieve data fusion of a first level; for the key area, establishing a refined twin substructure finite element model taking into account the effect of structural degradation and, on the basis of the inspection and monitoring data and the finite element influence line data of the key area, correcting the material attribute of the refined twin substructure finite element model to achieve data fusion of a second level; and calculating a correction force on the basis of the boundary condition of the key area and the material attribute, and using the correction force as an equivalent external load to act on a node of a global finite element model, thus achieving the interaction between a substructure and the main structure. The present invention achieves mutual fusion and common communication between the monitoring data and the finite element model and the interaction between each substructure and the main structure.

The invention provides a three-dimensional intelligent sorting method and system for dangerous goods logistics. Multi-frequency-band sound wave signals are emitted in a storage space through a sound wave standing wave array, a three-dimensional frequency coding grid is formed through standing wave interference, millimeter-level accurate positioning of a dangerous goods container is achieved, and a sorting execution instruction is generated. Airfoil piezoelectric ceramics of the aircraft receive sound wave signals and convert the sound wave signals into flight attitude parameters, a sorting path is planned in combination with a path tracking algorithm, meanwhile, a dynamic sorting channel opening is generated through airfoil flutter frequency and sound wave standing wave resonance, and a leakage container is accurately guided to be transferred to an isolation area. According to the method, the accuracy and efficiency of dangerous goods sorting can be remarkably improved, the risk of manual intervention is reduced, and a whole-process intelligent solution is provided for storage safety.

The invention discloses an agricultural machinery high-precision automatic driving intelligent control system, which belongs to the technical field of agricultural machinery vehicle automatic driving and comprises a data acquisition module, a path planning module, a control module and an instruction execution module. The data acquisition module is used for acquiring a dynamic data set in real time and transmitting the dynamic data set to the path planning module, and the path planning module is used for receiving and analyzing the dynamic data set and generating an optimal path; the control module is used for synchronously receiving the optimal path, the real-time vehicle body posture dynamic data and the environment change data and generating a control instruction set; the instruction execution module receives the control instruction set, synchronously adjusts the steering angle and the running speed of the agricultural machine, generates actual execution parameters and feeds back the actual execution parameters to the control module. Through arrangement of the control module, the data acquisition module, the path planning module and the instruction execution module, the collision hidden danger caused by insufficient environmental adaptability of a traditional fixed track algorithm is solved.

The invention discloses an anti-interference target tracking algorithm based on Transform. The algorithm comprises the following steps: collecting a video image; performing feature extraction and training on the sample data; continuously tracking the selected target; a coping strategy mechanism for appearance change, similar object interference, shielding and other phenomena; an attention module is used for increasing the extraction capability of the network on target features; the built target tracking network is trained; and if the interference phenomenon does not occur, the algorithm directly returns and displays the tracking result on the computer. If the algorithm judges that interference occurs, the algorithm adopts an interference coping strategy, and after interference influence is eliminated, a tracking result is returned and displayed on the computer. The method has high application value in the fields of unmanned aerial vehicle tracking, video monitoring, intelligent driving and the like, and interested objects appearing in tasks in the related fields can be tracked more continuously and accurately.

The invention discloses a self-adaptive environment image processing method for industrial gas detection, which relates to the field of industrial gas detection and comprises the following steps: extracting feature points of a current frame by utilizing a feature point detection algorithm according to image frames of an industrial monitoring video stream, and acquiring coordinates of feature points between frames by combining an optical stream tracking technology; generating affine transformation parameters, performing transformation decomposition on the coordinates of the feature points by using the affine transformation parameters, and extracting pose change parameters to obtain an image motion track; predicting an image motion track by using an image track processing algorithm, and comparing the predicted image motion track with a real-time image motion track generated by the current frame; and optimizing the affine transformation parameters, and carrying out frame-by-frame processing on the video stream by using the optimized affine transformation parameters. Through feature point detection and an optical flow tracking algorithm, image motion information in an industrial monitoring video can be extracted, and industrial application requirements of high real-time performance and high stability are met.

The invention provides an unmanned aerial vehicle laser obstacle clearance system and method for improving laser obstacle clearance efficiency, the obstacle clearance system comprises an unmanned aerial vehicle, an airborne computer carried on the unmanned aerial vehicle, a stability augmentation holder, a holder camera and a laser transmitter, and the unmanned aerial vehicle, the holder camera and the laser transmitter are all fixed on the stability augmentation holder; the pan-tilt camera is used for collecting a trunk swing video stream in real time and transmitting the video stream to the visual module of the airborne computer; the visual module extracts tree trunk skeleton tracking points in the video stream; a control module of the airborne computer generates a holder direction control instruction and a laser trigger signal according to the tracking point coordinate position; the stability augmentation holder carries out real-time attitude adjustment according to the holder direction control instruction; and the laser emitter performs laser cutting on the swing tree trunk after receiving the laser trigger signal. According to the invention, real-time tracking of the target local area of the swing tree trunk is realized by using the unmanned aerial vehicle and the tracking algorithm, and the laser obstacle removing precision and the temperature rising efficiency are improved.

The invention discloses a high-efficiency automatic parking system and method based on turning radius optimization, and the system comprises a sensor group which is used for obtaining the state information of a vehicle; the ECU is used for carrying out path planning operation based on a variable radius according to the vehicle state information and carrying out path tracking control operation based on preview distance self-adaption according to a planned path; the actuator is used for responding to the control operation of the ECU; according to the method, multiple parking strategies can be fused, rapid and accurate parking is achieved, and based on the R-S curve algorithm with the variable radius, garage rubbing or parking failure caused by the fixed radius is avoided; search is accelerated based on a simulated annealing algorithm, and a pure tracking algorithm is improved, so that the preview distance is adaptive according to the vehicle speed, the tracking precision and the parking stability are improved, and the automatic parking experience is optimized in all directions.