182 results about "Motion estimation" patented technology

The invention relates to the field of personnel action monitoring and recognition, and discloses an intelligent human shape trajectory prediction and alarm system and method based on multi-modal video analysis, and the system comprises a video collection module which is used for collecting video stream data of a monitoring scene; the data processing module is used for extracting motion state information of the target human shape; the trajectory modeling module is used for constructing a trajectory model of the target person; the risk assessment module is used for calculating a behavior risk value; the alarm module is used for triggering alarm; and the optimization module is used for optimizing the trajectory model. Target motion state information is extracted through the data processing module, high-precision input is provided for the trajectory modeling module, and the trajectory modeling module generates a prediction trajectory in combination with a dynamic optimization algorithm. Compared with a scheme depending on coarse-grained detection or simple position tracking in the prior art, the method has the advantages that fine modeling and dynamic prediction of the motion state are realized, the problem that complex behaviors cannot be accurately reflected is solved, and meanwhile, the track prediction precision and the system response capability are improved.

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

The invention discloses a visual inertial positioning method based on dynamic target detection and semantic information constraint, and belongs to the field of motion estimation and dynamic environment processing. According to the method, a dynamic target detection mechanism is introduced, the dynamic target is effectively detected based on the target detection network, inertial navigation information and geometric constraints, the dynamic target is effectively recognized in the image processing process, the corresponding dynamic feature points are screened out, the mismatching rate of the dynamic features is remarkably reduced, and high-quality observation input is provided for back-end optimization. In the back-end sliding window optimization stage, a semantic information consistency constraint method is constructed, and the estimation stability of the system in a weak texture area or a repeated texture area is enhanced by utilizing the consistency of feature points in the same semantic area on a geometric structure. Visual inertia pose estimation is realized based on dynamic target detection and semantic information constraint, and high-robustness and high-precision pose estimation can still be realized in a complex environment with dynamic interference of pedestrians, vehicles and the like and severe scene change.

An image enhancement method using multi-frame stacking comprises the steps of storing multiple frames of a target scene captured by a frame-based camera during an acquisition interval; storing a series of events produced during the acquisition interval by an event-based camera viewing the target scene; synchronizing timestamps of the events and timestamps of the frames; selecting an anchor frame among the multiple frames; aligning a current frame with respect to the anchor frame, based on stored events having timestamps corresponding to an interval between the anchor frame and the current frame; and merging the aligned frame and the anchor frame.

A computing system comprises a processor and memory storing instructions executable by the processor to obtain a sequence of image frames in a video. A target image frame of the sequence are input into a visual token embedding network of a trajectory-aware transformer to output a plurality of query tokens. A plurality of different image frames are input into a motion estimation network, the visual token embedding network, and a value embedding network to output a location map for each image frame, a plurality of key tokens, and a plurality of value embeddings, respectively. An image frame is selected the plurality of different image frames that has a closest similarity value from among the plurality of key tokens, and a super-resolution image frame is generated at a target time step as a function of an index query token, a value embedding of the selected frame, and the target image frame.

The invention discloses a mining equipment positioning system and method based on radar, ultra wide band and inertial navigation, and the system is characterized in that a multi-source sensor unit, a data synchronization and collection module and a preprocessing module are connected in sequence, and a state fusion module is connected with the preprocessing module, a UWB ranging error compensation module, a radar motion estimation module and a pose output module; the upper computer is connected with the pose output module; the method comprises the steps of collecting multi-source data; timestamp alignment and format conversion processing are carried out, and feature and speed constraint information extraction is carried out on the point cloud data; fitting correction is carried out on the ranging data by adopting a weighted least square method, and position information is obtained through an error weighted average algorithm; feature matching is carried out, and in combination with a KISS-ICP point cloud registration method, a speed estimation result is obtained; carrying out data fusion, and obtaining a pose estimation result by adopting an extended Kalman filtering algorithm; and outputting equipment position and attitude information. According to the invention, high-precision real-time estimation of the attitude and position information of the mining equipment can be realized.

The invention relates to an end-to-end automatic driving method based on Transform multi-modal feature fusion, and the method comprises the steps: collecting an RGB image and an original depth image, and converting the original depth image into an HHA image; inputting the RGB image and the HHA image into a sensing module for fusion to obtain a fusion feature map, and performing global average pooling and flattening operation to obtain environment features; acquiring the real-time speed of a vehicle, an advanced navigation command and a target position, connecting in series to form measurement input, and processing based on an MLP measurement encoder to obtain measurement characteristics; adding the environment features and the measurement features element by element to obtain combined features, performing downsampling step by step, and connecting a ReLU activation function behind each layer to obtain track features; and a loss estimator is constructed, training loss is dynamically predicted in real time based on the track features, and the weight of each feature parameter is dynamically adjusted according to the loss, so that the vehicle is dynamically controlled and adjusted in real time. According to the method, the multi-modal features can be well fused, so that a more accurate end-to-end automatic driving method is realized.

The invention relates to the technical field of advertisement shooting, in particular to a holder self-adaptive tracking control method based on visual identification, which comprises the following steps of: acquiring a scene image digital signal in real time through a camera, then extracting the coordinate, the size and the motion trail of a detection target, and adopting an optical flow motion estimation algorithm in the extraction process, the optical flow motion estimation algorithm can calculate the motion information of the target by analyzing the motion of the pixel points in the image, and is combined with pure extraction of static information such as coordinates and sizes, so that the state of the detected target can be described more comprehensively and dynamically, the location of the target can be known, and the motion of the target can be accurately mastered; and then, according to the motion trail of the detection target, calculating and establishing a mapping relation between a pan-tilt angle and a camera visual field, and calculating the angle of the pan-tilt needing to be adjusted when the detection target shoots the advertisement and the detection target moves through the mapping relation between the pan-tilt angle and the camera visual field, so that the detection target can be always located at the central point coordinate of the image digital signal.

The invention relates to the technical field of machine learning, and particularly discloses an intelligent parking guidance and reverse vehicle searching system based on multi-source heterogeneous data fusion. The system comprises a data perception fusion layer, a dynamic prediction decision-making layer and a user service interaction layer, realizes multi-step advanced probability prediction and dynamic optimal path planning of a parking space state through fusion of geomagnetic detection, video identification, payment flow, traffic situation and activity information, and realizes the optimal path planning of the parking space state based on multi-mode induction and live-action AR reverse vehicle searching guidance. And the parking efficiency and the user experience are improved.

The invention relates to a dynamic path planning method in a PFC-DWA complex environment based on an improved DWA algorithm, and belongs to the technical field of intelligent driving. The method is a window reduction algorithm for dynamic prediction in an environment with a large scene change rate. According to the algorithm, the kinematics constraint of the Ackerman steering trolley is considered, a dynamic obstacle collision evaluation window reduction strategy according to the dynamic obstacle and intelligent vehicle motion state information is provided, the dynamic window range is optimized through the motion state information and a kinematics constraint equation by using an analytic geometry method, and the dynamic obstacle avoidance capability of the intelligent vehicle is improved. In order to solve the problem that the evaluation function of the DWA algorithm lacks evaluation of the obstacle state change trend, the proposed algorithm predicts the future position information of the obstacle based on the least square method according to the historical position information of the obstacle, the future position information is fused into the evaluation function of the algorithm, and the evaluation performance of the algorithm on the simulated trajectory in the dynamic environment is improved. The problem of low arrival rate of a path planned by a conventional algorithm in a frequent scene change environment is solved.

An image processing apparatus is configured to execute the following steps. A first motion estimation is performed on a down-sized current frame and a down-sized reference frame to generate first motion vectors and a first reliability corresponding to the first motion vectors. An n-th motion estimation is performed on a current frame and a reference frame based on the first motion vectors and the first reliability to generate n-th motion vectors and a n-th reliability corresponding to the n-th motion vectors. A compensated frame between the current frame and the reference frame is generated based on the n-th motion vectors and the n-th reliability.

The invention relates to the technical field of intelligent traffic, and discloses a multi-modal large model context understanding method for a high-speed abnormal exposure scene, which comprises the following steps of: 1, aiming at a complex space-time structure of video data, encoding by adopting a RoPE (Rotation Position Element) variant to obtain a time sequence characteristic, and 2, combining the time sequence characteristic with a text characteristic to obtain a multi-modal large model context understanding result; the method comprises the following steps of: 1, inputting time sequence + text multi-modal data, 3, dynamically predicting the weight of a Laplacian pyramid decomposition kernel according to the characteristics of the input multi-modal data, 4, extracting and converting the characteristics in each layer of a characteristic pyramid, reconstructing the characteristics to original resolution layer by layer, and generating a corrected image, and 5, in the training process, generating a corrected image. And 6, inputting the corrected image into an event understanding module, identifying and classifying various events in the scene, and realizing semantic understanding.

The invention belongs to the technical field of visual angle follow-up vehicle panoramic look-around, and particularly relates to a visual angle follow-up vehicle panoramic look-around system based on dynamic prediction, which is characterized in that monocular vision and laser radar time sequence data are fused, and a dense and stable dynamic target depth sequence is generated through dynamic target initial perception, cross-modal time sequence alignment and inter-frame depth correlation modeling; through environment interaction modeling and target behavior intention prediction double-branch parallel processing, a dynamic self-adaptive accurate track is output in combination with a weighted fusion strategy, and the prediction weight dynamic adjustment requirement of a complex motion scene is adapted; after a virtual view angle image is preliminarily synthesized based on the predicted trajectory, deviation detection and coordinate dynamic correction are realized through inter-frame target consistency verification, and artifact area positioning and pixel-level restoration are synchronously completed; multi-modal preprocessing and algorithm tasks are deployed according to hardware, and a model lightweight optimization and inter-frame prediction result multiplexing strategy is combined, so that low-delay operation of the system in a high-resolution panoramic image output scene is ensured.

Systems and methods for reconstructing medical images based on motion estimation are disclosed. Measurement data from positron emission tomography (PET) measurement data, and modality measurement data from an anatomy modality, such as computed tomography (CT) data, is received from an image scanning system. A trained deep learning process is applied to the PET measurement data and the modality measurement data to generate displacement vector field (DVF) data characterizing motion between the PET measurement data and the modality measurement data. A modality image is reconstructed from the modality measurement data, and the modality image is adjusted based on the DVF data. A PET image is then reconstructed from the PET measurement data and the adjusted modality image, and the PET image is adjusted based on a computed inverse of the DVF data. The adjusted PET image and the modality image spatially match, and are displayed.

The invention discloses a cracking furnace intelligent optimization system control method and system architecture, and belongs to the cracking furnace application field, and the cracking furnace intelligent optimization system control method comprises the following steps: S1, collecting cracking furnace operation time sequence characteristic data and flame combustion video data in real time through a sensor and a high temperature camera; s2, preprocessing the collected multi-modal data, S3, integrating the preprocessed multi-modal data by using federal learning, and constructing a cross-modal causal logic model; s4, designing a deep reinforcement learning framework, introducing the dynamic prediction model of the cracking furnace, and reasoning the recommendation range of each parameter of the optimized operation of the cracking furnace; s5, dynamic process data and the environment-friendly emission prediction model are combined, a data-driven MPC algorithm framework is adopted, and dynamic optimization control over the cracking furnace is achieved; and S6, the operator performs adjustment or selects emergency operation according to the recommended parameters and strategies to realize man-machine cooperative control. Finally, the dynamic optimization control of the cracking furnace can be realized, and the energy consumption is effectively reduced.

The invention relates to the technical field of security and protection monitoring systems, in particular to a full-scene intelligent analysis system and method based on a multi-modal large model, and the system comprises a feature extraction module which is used for obtaining cross-modal data, generating a time sequence feature set according to a cross-modal tensor, and generating a space structure set according to cross-modal image data; the semantic fusion module performs semantic interaction on the time sequence feature set and the space structure set based on a cross-modal structure to obtain a cross-modal data set; the causal generation module generates causal association based on the time sequence feature set and the spatial structure set; generating a causal time sequence association based on the cross-modal data set and the causal association; the dynamic prediction module associates the dynamic learning association mechanism based on the causal timing to generate a dynamic prediction model. According to the invention, the intelligent supervision capability of key places is obviously improved, and the generalization detection capability of novel violation behaviors can be improved, so that complex or unknown risk scenes are covered.

The invention relates to the technical field of intelligent equipment scheduling planning, and discloses a scene scheduling method and system for intelligent equipment, and the method comprises the steps: determining the task emergency degree and the motion complexity based on a dual-domain scheduling factor construction mechanism; constructing a scheduling priority weight; screening a conflict node set; generating an avoidance decision by adopting a double-case evaluation mechanism; and finally, synchronously issuing the avoidance decision to related intelligent equipment. In the prior art, there is no scheduling method of a priority guidance and fine conflict judgment mechanism, and especially in a dense operation scene of multiple mobile devices including an industrial AGV, a luggage transfer robot and a mobile charging vehicle, dynamic prediction and active avoidance of conflict risks are difficult to realize. Due to the fact that task and motion double-domain factor construction and a space and time double-condition conflict recognition mechanism are introduced, active avoiding and real-time response of device-level scheduling are achieved, and the cooperative operation stability of intelligent devices is improved.

The invention discloses a mechanical arm intelligent grabbing system and method based on three-dimensional vision. The system comprises a three-dimensional vision system, an image preprocessing module, a model matching module, a grabbing point generation module, a grabbing control module and a dynamic environment self-adaption module. The method comprises the steps of multi-modal reference model collection and feature library establishment, precise hand-eye calibration and grabbing strategy self-learning and multi-modal fusion recognition, and dynamic prediction and self-adaptive grabbing. By adopting the three-dimensional visual recognition system and combining algorithms such as point cloud processing, point cloud method phase generation, point cloud downsampling, rough model matching and fine model matching, the limitation that a traditional mechanical arm system depends on two-dimensional image training and position coordinate point matching is overcome; by generating the grabbing points and calculating the pose coordinates of the grabbing points according to the hand-eye calibration matrix, the problem that the estimated pose of the grabbed object under the camera coordinate system is inaccurate in the prior art is solved. The system is easy to implement and has high practicability and popularization value.

The invention belongs to the technical field of artificial intelligence, and discloses a hidden space world model construction method for a robot grabbing operation scene and related equipment thereof. The method comprises the steps of performing semantic segmentation on a scene image through an image semantic segmentation network to generate a semantic segmentation mask of each object; processing the semantic segmentation mask through a multi-task scene understanding network to generate implicit representation; based on the implicit representation of each object at the current moment and the action information of the mechanical arm, predicting implicit representation at the next moment through a state transition network; and according to the predicted implicit representation at the next moment, generating a scene segmentation image reconstruction result, an object existence judgment result and a contact relation judgment result through a decoder in the multi-task scene understanding network architecture, and constructing an implicit space world model of the scene. Based on the method, efficient characterization and dynamic prediction of the robot grabbing operation scene are achieved, and the generalization ability and the multi-task cooperation efficiency of the model in a complex environment are improved.

The invention discloses a three-dimensional motion estimation method based on an event camera. The method comprises the following steps: 1, constructing a time-space decoupling event time sequence and spatial representation; 2, geometric structure and context features are extracted through a space encoder, and time sequence evolution features are extracted through a time sequence encoder; 3, respectively constructing a spatial correlation graph and a time sequence correlation graph based on the geometric structure characteristics and the time sequence evolution characteristics; 4, constructing a pixel track curve according to the event density; 5, geometric structure and time sequence evolution characteristics are sampled along the pixel track curve, and a space-time correlation graph is generated; 6, iteratively optimizing pixel track curve parameters according to the space-time correlation graph and the context features; and 7, utilizing the optimized pixel track curve and the derivative thereof to accurately predict the three-dimensional motion. The method obviously improves the three-dimensional motion estimation precision of the event camera in a complex scene, is suitable for the fields of automatic driving and robots, and has a good engineering application value.

A computer system comprising processing circuitry configured to determine vehicle motion information of a vehicle is provided. The processing circuitry is configured to receive first sensor data and second sensor data for estimating vehicle motion with respect to one or more common reference entities. The first sensor data has sensor data of a first type. The second sensor data is sensor data of a second type. The first type differs from the second type. The processing circuitry determines a first quality metric associated with the first sensor data. The processing circuitry is determines whether to use the first sensor data, the second sensor data, or a combination thereof as a basis for determining the vehicle motion information based on the first quality metric associated with the first sensor data.

The invention discloses a visual logging video depth calibration method based on coupling data and inter-frame motion estimation, and relates to the technical field of storage-type visual logging in the petroleum and petrochemical industry. The visual logging depth calibration method has higher accuracy than the existing distance measurement, and solves the problems of logging cable elongation and time asynchronism. The method automatically identifies casing couplings and records the position of the image where the coupling appears in the video by using edge detection technology, feature parameter extraction, feature matching and intelligent discrimination. The method intelligently and automatically extracts coupling parameters, and calculates the distance from the coupling to the camera by using the proportional relationship between the camera imaging distance and the imaging size. The method interpolates coupling data by using a method based on inter-frame motion estimation to obtain depth data between couplings. The method automatically merges the accurately calibrated data with the video, and performs intelligent matching to ensure data accuracy.

The invention provides an HRRP target micro-motion characteristic extraction method based on chaos optimization and polarization synthesis, and aims at the field of high-resolution one-dimensional range profiles. The method comprises the following steps: performing optimal polarization contrast enhancement and optimal polarization fusion based on a maximum signal-to-noise ratio criterion on HRRP multi-channel polarization data; performing period estimation on the polarization fusion data in combination with second harmonic back-off optimization and autocorrelation / amplitude difference enhancement processing; chaotic mapping and back propagation are combined to avoid the problem that traditional optimization is prone to falling into local minimum, and precession angle and size results of the space target are obtained. According to the method, the feature contrast of HRRP data is remarkably improved through polarization fusion, meanwhile, high-precision estimation of the micro-motion features of the space target is achieved under the condition of a low signal-to-noise ratio, the stability of periodic detection within limited observation time and the global search capability of parameter inversion are effectively improved, and the method is suitable for large-scale popularization and application. And the accuracy and robustness of space target micro-motion estimation in a complex environment are obviously enhanced.

The invention discloses an image-based urban solid waste (MSW) calorific value prediction method. According to the method, on the basis of a monitoring video of a garbage storage pool, an in-furnace garbage image is captured and screened manually, and an efficient multi-module fusion learning framework is developed by using a deep learning technology. According to the framework, through extracting time sequence features and image visual features of calorific value labels and introducing a variational auto-encoder to process high-dimensional combined features, dynamic prediction of the incineration calorific value of the garbage entering the furnace can be realized. According to the actual working condition, two different time sequence information input strategies are explored, one strategy is that the real heat value at the previous moment is used as input, the other strategy is that the predicted heat value at the previous moment is used as input, and effective information reference and decision support can be provided for incineration intelligent control of the waste incineration power plant.

The invention discloses a lower limb motion capture method and device based on a flexible pressure sensor array and a medium, and the method comprises the steps: collecting foot pressure signals generated by two feet when a human body moves through the flexible pressure sensor array arranged on an insole; the collected foot pressure signals are input into a pre-trained action estimation model, coordinates of lower limb joint points are predicted and output, and then real-time human body lower limb movement conditions are obtained; wherein the predicted lower limb joint points comprise ten key points, namely hip joints, knees, ankles, heels and toes of the left limb and the right limb. By combining the flexible pressure sensor array and the motion estimation model, the motion position of the lower limb joint is estimated only based on the flexible pressure sensor on the insole, and the problems of high cost and complicated wearing in the prior art are solved. The method can be widely applied to the technical field of motion capture.

An image acquisition method is generally provided, comprising the steps of providing a frame-based camera (10) having a field of view; storing a series of events produced during an acquisition time interval (∆T, Texp) by an event-based camera (12) viewing a target scene in the field of view of the frame-based camera; estimating motion (16) in the target scene based on the events stored during the acquisition interval; determining an exposure time (Texp) based on the estimated motion; and applying the exposure time to the frame-based camera (10) for capturing a frame.

The invention discloses a water conservancy and hydropower potential safety hazard intelligent identification method and system based on image identification, and relates to the technical field of water conservancy and hydropower engineering management. The method comprises the following steps: acquiring a real-time image data sequence and an engineering facility digital twinborn model; performing space mapping and timestamp alignment on each frame of image and the digital twin model to generate a registered time-space image data set; layered feature extraction is carried out on the registered time-space image data set, and structured engineering defect features are analyzed from potential abnormal targets in combination with three-dimensional geometric structure information of a digital twin model; performing association analysis on the engineering defect features and engineering attribute information and historical operation and maintenance data of the digital twin model to judge hidden danger types of the engineering defects, and calculating risk levels of the engineering defects according to a preset risk assessment matrix; and generating a structured potential safety hazard report. The method realizes high-precision context sensing of potential safety hazards of water conservancy and hydropower and has dynamic prediction capability.

The invention relates to a visual positioning method and device based on a panoramic camera, equipment and a storage medium. The method comprises the following steps: acquiring a panoramic image through a double-fisheye camera; performing SPHORB feature point extraction on the panoramic image, and matching feature points between two continuous frames of panoramic images by using an optimized feature matching algorithm to obtain matched feature point pairs; based on the feature point pairs, calculating relative motion of the double-fisheye camera between two continuous frames of panoramic images by using a motion estimation algorithm to obtain a pose transformation matrix of the double-fisheye camera; and optimizing the pose transformation matrix by using a bundle adjustment optimization algorithm to obtain an optimized pose transformation matrix. According to the embodiment of the invention, the double-fisheye camera is used for shooting the panoramic image, the visual range of the camera is expanded, more comprehensive environment information is obtained, feature point extraction is carried out on the panoramic image, and the visual positioning precision of equipment is improved.

The invention relates to the field of intelligent medical treatment, in particular to a dynamic illumination prediction method and device based on a refraction state and a program product. Comprising the steps of obtaining eye refraction data of a to-be-tested person; carrying out clustering and feature fusion on the refractive data to obtain fusion features; outputting the fusion features to a trained prediction model for prediction to obtain a brightness prediction value and a color temperature prediction value; and the prediction model carries out weight parameter initialization by compressing an abnormal value of refractive data and compensating distribution deviation of refractive clustering. According to the method, personalized expression of different myopia degrees on illumination perception differences can be achieved, brightness and color temperature parameters are dynamically predicted, actual lighting equipment is driven, dynamic balance of visual comfort and environmental adaptability is achieved, and the method has good actual application value.

A system including an internal sensor configured to generate first sensor data, an external perception sensor configured to generate second sensor data, at least one memory configured to store computer executable instructions, and at least one processor is disclosed. The at least one processor is coupled to the internal sensor, the external perception sensor, and the at least one memory. The at least one processor is configured to execute the computer executable instructions to: (i) compute a first velocity output based upon the first sensor data; (ii) compute a second velocity output based upon the second sensor data; (iii) compute a relative velocity between the first velocity output and the second velocity output; (iv) determine the relative velocity is outside a predetermined threshold value; and (v) perform an intervening action in response to the determining.