95 results about "Correlation filter" patented technology

The invention discloses a ship target detection and tracking method and system based on video image processing, and the method comprises the steps: 1), collecting a channel panoramic video stream through a high-definition camera, and extracting a continuous image sequence; 2) constructing a dynamic channel background model by using a Gaussian mixture model (GMM), and updating the background in real time to eliminate interference of water surface fluctuation and illumination variation; (3) enhancing the data obtained in the step (1) by adopting a Mosaic enhancement method, and establishing a data set; 4) optimizing a Yov7 model, and introducing a lightweight CBAM attention mechanism to carry out model training; (5) the trained weight model is experimented on the verification set, and target detection of the ship is achieved; and 6) adopting a kernel correlation filtering algorithm to realize real-time tracking of the moving ship. According to the method, the technology in the field of computer vision is utilized, the real-time video image number of the large-flow channel is effectively utilized, multi-target recognition and real-time tracking are achieved, and the method has high accuracy and robustness.

An unmanned aerial vehicle target detection tracking method based on intelligent image matching comprises the steps that an unmanned aerial vehicle collects images to make a data set, a model file is obtained through twin neural network training, the model file is loaded to detect a video stream, a current frame target position and detection frame information are obtained, whether the frame is a first frame is judged, and if yes, the target is detected; using a target position information result to initialize a kernel filtering algorithm, and constructing a measurement vector; if not, calculating correlation by using a kernel correlation filter, taking the highest response position as a prediction bit, and constructing a measurement vector; calculating a Kalman gain, and inputting the Kalman gain into a Kalman filter to obtain a predicted state estimated value = (); comparing y-axis deviations of the detector and the tracker, and setting a threshold value for judgment; according to the method, target tracking under resource limitation is realized through light-weight twin network detection in combination with kernel filtering and improved Kalman filtering, the problems of high target loss rate and low precision are solved, and the method is suitable for the fields of military reconnaissance, intelligent autonomous attack and the like.

The invention discloses a space-based distributed multi-sensor multi-target tracking and positioning method. The method comprises the following steps: firstly, acquiring a grayscale image of each sensor, and extracting coordinates of a target in the image; and matching a serial number for the extracted target, and taking a first frame coordinate and an image sequence of the target as tracking input. The tracking process is based on a discriminant correlation filter, an energy concentration threshold value of small target features is added, a double-constraint trajectory correlation model of target spacing and speed similarity between continuous frames is constructed, and a Kalman filtering algorithm is used to predict a target position. An adaptive covariance cross fusion positioning algorithm is designed among multiple sensors, a geometrical relationship between multiple detection satellites and a target is established, and the three-dimensional position of the target is calculated by using sensor observation parameters as a basis for adjusting fusion weights. The method can significantly improve the target tracking precision and suppress the positioning offset problem caused by instability of part of sensors, can adapt to the dynamic change of a distributed detection scene, and ensures the accurate target positioning.

The application discloses a structure interlayer displacement automatic measurement method and system based on visual data and belongs to the technical field of structure displacement measurement. The measurement method comprises the following steps: acquiring video data in the vibration process of a building based on a monitoring camera; processing the video data by using an improved kernel correlation filtering algorithm to extract building interlayer displacement information; verifying the extracted displacement information and outputting building interlayer displacement results. On the basis of a classical kernel correlation filtering target tracking method, the application utilizes image pyramid, robust feature extraction and discrete Fourier transform up-sampling technology to expand the tracking range, enhance the edge features and improve the tracking precision; the application fully utilizes the existing monitoring camera equipment to measure the interlayer displacement, does not need to additionally arrange sensors and reduces the cost budget.

The audio device comprises a receiver (201) arranged to receive a set of input audio signals. An audio beamformer (101) performs beamforming by combining the outputs of the filters. The feedback circuit (103) comprises a set of matched filters having a complex conjugate frequency response, and the beamforming adapter (105) adapts the filters in response to a comparison of the input audio signal and the feedback audio signal. An adaptation coefficient processor (207) determines decorrelation coefficients for a set of spatial decorrelation filters that generate a decorrelated output signal from an input audio signal, where the decorrelation coefficients are adapted based on the input signal. A set of spatial filters (205, 301, 401) is arranged to apply spatial filtering to the input audio signal or the feedback signal, wherein the set of spatial filters has coefficients determined from the decorrelation coefficients.

The invention provides an underwater target speed measurement method and device based on template correlation, and the method comprises the steps: obtaining an echo signal of a transmitting signal, the transmitting signal comprises a frequency modulation signal and a single-frequency pulse signal, and the transmitting signal forms the echo signal after being reflected by a target; determining a target frequency spectrum and a template frequency spectrum according to the echo signal, the frequency modulation signal and the single-frequency pulse signal; performing correlation filtering analysis on the target spectrum and the template spectrum to obtain a search spectrum; according to the transmitting frequency of the single-frequency pulse signal, determining the Doppler frequency corresponding to the maximum amplitude value from the search range of the search spectrum; and calculating the radial speed of the target according to the Doppler frequency and the transmitting frequency of the single-frequency pulse signal. Therefore, the accuracy of determining the movement speed of the target is improved.

This invention discloses a method for inspecting the assembly quality of parts in confined spaces based on 3D enhanced correlation filtering, relating to the field of machine vision. The method includes: acquiring template images of correctly assembled parts and marking the locations of the parts as regions of interest; extracting region features and training a correlation filter; obtaining the overall correlation filter response using features from the tested image; performing multi-feature adaptive fusion based on preset evaluation metrics; correcting the fused correlation filter response using a depth-sensing 3D enhancement method; determining whether a part is occluded based on the fused correlation filter response; dividing the identified occluded region into sub-regions, detecting each sub-region to obtain the fused correlation filter response, and weighting and correcting it; determining the unoccluded areas of the part based on the enhancement results; and determining whether the part is incorrectly installed based on the 3D enhanced correlation filter response. This invention effectively solves the problems of incorrect, missing, and incomplete installation of parts in confined spaces with occluded targets.

A system can include a distributed acoustic sensing (DAS) system and a computing device coupled with the DAS system. The DAS system can include a pulse generator and a sensing fiber that can be positioned in a wellbore. The system can generate a light pulse to be transmitted into the sensing fiber to make one or more measurements relating to a wellbore operation involving the wellbore. The system can receive an initial pulse that includes a reflection of the light pulse prior to the light pulse entering the sensing fiber. The system can apply a cross-correlation filter to one or more subsequently received pulse reflections originating from the sensing fiber. The cross-correlation filter can include the initial pulse reflection or synthetic initial pulse. The system can generate, based on applying the cross-correlation filter, an output signal that can be used to control the wellbore operation.

This invention relates to an object detection device. The object detection device (1) includes: a drive signal generation unit (5) that generates a drive signal including frequency modulation; a first correlation filter (62) that performs correlation detection between the received signal and a first reference signal corresponding to the drive signal; a first determination unit (8) that determines whether the received wave is a reflected wave of a detection wave transmitted from a transmitting unit (40A) based on the correlation signal of the first correlation filter; a second correlation filter (63) that uses a signal corresponding to a portion of the drive signal as a second reference signal and performs correlation detection between the received signal and the second reference signal; a third correlation filter (64) that uses a signal including a portion of the drive signal with a frequency higher than the second reference signal as a third reference signal and performs correlation detection between the received signal and the third reference signal, wherein the third reference signal is a signal corresponding to a portion of the drive signal; and a second determination unit (9) that determines whether an object is within the detection range based on the correlation signals of the second and third correlation filters.

This invention provides an intelligent cockpit interaction management method, relating to the field of data processing technology. The method includes: receiving voice interaction data, gesture interaction data, and facial expression interaction data collected in the cockpit; performing intent parsing to extract triggering conditions and target functions; combining vehicle operating status parameters, in-vehicle noise level parameters, and driver gaze area parameters to perform correlation filtering; performing conflict detection to determine and remove interaction intents with abnormal triggering conditions or conflicting target functions as invalid; classifying tasks according to the category of target functions to generate a task candidate queue; confirming tasks in the task candidate queue and generating a valid task queue for tasks that meet the triggering conditions; and performing task scheduling according to the valid task queue, interrupting low-priority tasks and switching execution when a higher-priority task input is detected. This invention improves the accuracy of cockpit interaction management.

In various examples, image regions can be extracted from a batch of one or more images, and the image regions can be scaled to one or more template sizes in batches. In cases where the image region includes a search region for object localization, the scaled search region can be loaded into the Graphics Processing Unit (GPU) memory and processed in parallel for localization. Similarly, in cases where the image region is used for filter updates, the scaled image region can be loaded into the GPU memory and processed in parallel for filter updates. The image regions can be processed in batches from any number of images and / or from any number of single-object and / or multi-object trackers. Other aspects of the present disclosure provide methods for associating locations using correlation response values, methods for enabling a correlation filter to learn in object tracking based at least on focus windowing, and methods for enabling a correlation filter to learn in object tracking based at least on an occlusion map.

The object detection device of the present application is provided with: a transceiver (4) that transmits an ultrasonic wave encoded by frequency modulation and receives the ultrasonic wave and outputs a reception signal; a first quadrature detector (61) that generates a complex reception signal by quadrature detection of the reception signal and outputs it; a second quadrature detector (71) that generates a complex reference signal by quadrature detection of a reference signal and outputs it; a correlation filter (62) that performs correlation detection of the complex reception signal and the complex reference signal and outputs a correlation signal; and a code determination unit (8) that determines a code contained in the reception signal based on the correlation signal.

The invention discloses a narrow space part assembly quality detection method based on three-dimensional enhanced correlation filtering, and relates to the field of machine vision. Comprising the following steps: acquiring a template image of a correctly assembled part, and marking the position of the part as a region of interest; extracting regional features and training a correlation filter, and detecting the features of the detected image to obtain an overall correlation filter response; performing multi-feature adaptive fusion based on a preset evaluation index; correcting the fusion correlation filtering response by using a depth perception three-dimensional enhancement method; judging whether the part is shielded or not according to the fused correlation filtering response; dividing the area which is judged to be shielded into sub-areas, detecting each sub-area to obtain a fusion correlation filtering response, and carrying out weighted correction on the fusion correlation filtering response; judging an unshielded area of the part according to an enhancement result; and judging whether the parts are installed wrongly according to the three-dimensional enhanced correlation filtering response. The problems of wrong installation, neglected installation and not-in-place installation of parts under the condition of target shielding in a narrow space are effectively solved.

A method is provided. The method comprises: obtaining HR filter data indicative of a set of head-related HR filters for generating audio corresponding to temporal and / or spectral variations of sound waves; determining a time interval for evaluating HR filters included in the set of HR filters; and evaluating at least one HR filter included in the set of HR filters using the determined time interval. Audio is generated based on the evaluation.

A signal processing method for processing audio signals and in particular for adjusting the tone of music signals is disclosed. The method provides adaptive tonality control. Here, a shape dependent filter magnitude response is calculated, a level dependent filter magnitude response is calculated, a result response is calculated based on the shape and level dependent filter magnitude responses, and an output signal is calculated by convolving an input signal with the result response. In particular, the calculation of the shape dependent filter magnitude response comprises at least the steps of calculating a magnitude spectrum of a signal, filtering the resulting magnitude spectrum using a first smoothing filter, applying a predetermined weighting curve to the spectrum, detrending the weighted spectrum by fitting a curve to said weighted spectrum and subtracting said fitted curve from said weighted spectrum, and inverting the resulting spectrum to form the shape dependent filter magnitude response.

In various examples, a technique for computing feature correlations given a stereo image pair (captured using two or more image sensors having at least partially overlapping fields of view) is disclosed. The technique includes, for one or more channels in a set of feature channels, receiving a first feature map for a first image in a stereo image pair and a second feature map for a second image in the stereo image pair and computing a corresponding set of correlation maps. The technique also includes generating a set of compressed correlation maps; masking one or more portions of individual compressed correlation maps of the set compressed correlation maps based at least on a respective correlation filter to generate a corresponding set of masked correlation maps; and generating a depth map associated with the stereo image pair based at least on the set of masked correlation maps.

The application relates to a target tracking method for a high-speed rotating platform, which comprises the following steps: acquiring an initial frame image for target tracking; acquiring a next frame image, performing image rotation according to a rotation angle existing in the initial frame image, and obtaining a rotated image eliminating the rotation angle; taking a target point coordinate of a previous frame image as a center, adopting a correlation filtering model to perform target detection in the rotated image, and obtaining a target point coordinate in the rotated image; converting the target point coordinate in the rotated image to a target point coordinate in a pre-rotated image, and taking the target point coordinate in the pre-rotated image as a center to obtain a search region; updating the correlation filtering model, and performing target tracking on a next frame image; and further performing target tracking on all subsequent frame images. The application realizes stable tracking in the high-speed rotating process of the image.

The application discloses a visual target tracking system and method based on a nano unmanned aerial vehicle, aiming at the problem that a traditional kernel correlation filter tracking algorithm cannot adapt to target size change, introduces a scale filter, so that the tracking algorithm can adaptively adjust the size and position of a target frame according to the change of the target scale; a method based on time context information is adopted to realize template updating, so that the tracking template has time correlation and can more accurately reflect the change of the target in appearance and motion state; a long-short-term tracking strategy is adopted to combine the information of short-term tracking and long-term tracking, so that the target tracker can still maintain high precision and robustness in a long-time tracking task, and the real-time requirement is met; the application is experimented on a Crazyflie2.1 nano quadrotor platform, and realizes stable and rapid target tracking control on the nano quadrotor unmanned aerial vehicle.

The present invention discloses a method for tracking hidden targets behind walls applicable to low signal-to-noise ratio environments, which is applied to the field of radar tracking technology. Aiming at the problem in the prior art that in a complex electromagnetic environment, there is a lot of clutter and noise in the echo, resulting in poor imaging quality and thus difficult target tracking. The present invention first reconstructs the scene by using the back-projection method. Then, it extracts the target area features and establishes a target template based on the ridge regression idea, establishes a scale pool with small scale change in the range direction and large scale change in the azimuth direction based on the through-wall radar target imaging mechanism and the characteristics of the target after imaging. Then, it tracks the target in the next frame based on the correlation filtering idea, updates the target template for each frame to adapt to the changes in the through-wall radar target features, and finally dynamically estimates the target area and obtains the tracking trajectory. The present invention can achieve precise tracking of hidden targets behind walls in low signal-to-noise ratio scenarios.

The application provides a template pool matching-based anti-occlusion tracking and re-detection system and method, which comprises two parts of a template pool tracking module and a sparse matrix re-detection module; the template pool tracking module is mainly optimized for a traditional kernel correlation filter algorithm, an adaptive feature fusion strategy is used to cope with scenes rich in texture information and scenes rich in color information respectively, and APCE and PSR two criteria are combined to judge the occlusion condition in the tracking process; the template pool strategy stores and compares the space-time context information to cope with the occlusion scene; the sparse matrix re-detection module searches the possible positions of the target through a sparse sampling method in multiple threads to achieve the purpose of fast recapture when the target loss occurs.

A method for generating a head-related (HR) filter for audio rendering is provided. The method comprises generating HR filter model data which indicates an HR filter model, and based on the generated HR filter model data, (i) sampling one or more basis functions and (ii) generating first basis function shape data and shape metadata. The method further comprises providing the generated first basis function shape data and the shape metadata for storing in one or more storage mediums.

A sensor circuit (620) for measuring physical quantities, comprising: a signal acquisition circuit (640) including sensors (621, 622) for providing input signals (v1, v2) related to the physical quantity; and a processing circuit (650) for receiving the input signals (v1, v2) and providing an output signal (θ) representing the physical quantity. 校正 The processing circuitry includes a closed loop comprising: a first sub-circuit (660) arranged to receive the input signals (v1, v2) and feedback signals (fbi, fbq), and configured to provide a first signal (θ). 估计 ); a frequency correlation filter (632) for receiving and filtering the first signal (θ) 估计 ), and is used to provide the output signal (θ) 校正 ); a second sub-circuit (670), which is used to receive the filtered signal (θ) 校正 ) and use a nonlinear function to filter the signal (θ) 校正 ) are converted into the feedback signals (fbi, fbq).

The present invention relates to a device and method for measuring the surface oil film thickness of a stamping part based on correlation filtering. The measuring device includes an infrared light source, a focusing lens group, a filter wheel disc, and a detector. A normal absorption window and a saturation absorption window are provided on the filter wheel disc. The rotation of the filter wheel disc can make the normal absorption window and the saturation absorption window rotate to the optical path that can be detected by the detector. Both the normal absorption window and the saturation absorption window are sealed by two transparent window pieces, and no filling is done between the two transparent window pieces that encapsulate the normal absorption window. A measured oil film sample with a certain thickness is encapsulated between the two transparent window pieces of the saturation absorption window.

The application discloses a target tracking method and system combining correlation filtering and potential matching, and relates to the technical field of computer vision. The method generates a correlation filter and an image template by using a target image, and generates a boundary potential field, a depth field and a mask template of a point cloud template by using a target point cloud. Then, the method performs correlation operation on the image template and an environment image by using the correlation filter to obtain a potential region of the target. Then, the boundary potential field and the depth field in the potential region are rasterized, and local statistical information is calculated. Finally, potential matching is performed on the point cloud template and the potential region by using the local statistical information to obtain an accurate position of the target. The application fuses a visual sensor and a laser radar, and has high tracking accuracy and stability in scenes such as target deformation, illumination change and motion blur. Meanwhile, the application adopts a 'from coarse to fine' tracking mode, realizes the complementary advantages of vision and point cloud, and has high execution efficiency and reliability.

To provide a method, an apparatus, and a computer program product for efficiently generating a head-related (HR) filter for audio rendering.SOLUTION: A method includes: generating HR filter model data indicative of an HR filter model; sampling one or more basis functions on the basis of the generated HR filter model data; generating first basis function shape data and shape metadata on the basis of the generated HR filter model data; providing the generated first basis function shape data and shape metadata for storing in one or more storage media; detecting an occurrence of a triggering event; and outputting second basis function shape data and shape metadata for audio rendering as a result of detecting the occurrence of the triggering event.SELECTED DRAWING: Figure 11

The invention discloses calculating feature correlation to estimate depth information for stereoscopic images. In various examples, a technique is disclosed for calculating a feature correlation for a given stereoscopic image pair (captured using two or more image sensors having at least partially overlapping fields of view). The technique includes, for one or more channels in a set of feature channels, receiving a first feature map of a first image in a stereoscopic image pair and a second feature map of a second image in the stereoscopic image pair, and calculating a corresponding set of correlation maps. The technique further includes generating a set of compressed correlation maps; masking one or more portions of each of the compressed correlation maps in the set of compressed correlation maps based at least on a respective correlation filter to generate a corresponding set of masked correlation maps; and generating a depth map associated with the stereoscopic image pair based at least on the masked correlation map set.

The present invention discloses a video SAR target tracking method and system based on a spatiotemporal expansion-accumulation strategy, which divides the video SAR image data set into N Batch processing packages; extract the features of the initial position through the background perception correlation filter, obtain the initialization parameters and initial filter template of the background perception correlation filter, and generate the initial candidate frame; based on the candidate frame of the first frame, define the target search area of ​​the second frame to obtain multiple test samples, calculate the similarity and obtain the response matrix; select S The target prediction position corresponding to the maximum response value; extract features for each prediction position and update the corresponding template to generate S candidate boxes; repeated iterations are performed to obtain candidate boxes, forming a multi-frame tree structure; reverse search is performed for the estimated position of the target in each frame in the tree structure to obtain candidate paths for the target trajectory; the candidate path with the largest value function is selected as the path of the target in a batch package, and the estimated state of the target in each frame is output.

The present invention relates to a correlation filtering target tracking method, system, and storage medium, belonging to the field of computer vision technology. The method solves the problems of existing technologies in tracking targets in complex scenes, high computational cost, and poor real-time performance. The method includes: using the current correlation filtering model and the frequency domain features of the current frame image to perform a correlation operation to obtain a first response map and determine the current target position; using the current target position as a reference, segmenting the current frame image to obtain a new window image and extracting its frequency domain features; optimizing the objective function using the first response map and the frequency domain features of the new window image to obtain the correlation filtering model corresponding to the next frame image; using the correlation filtering model corresponding to the next frame image and its frequency domain features to perform a correlation operation to obtain a second response map, and obtaining the target position of the next frame image based on the second response map. Based on the frequency domain features of the image, the method can track targets in complex environments, with low computational cost and high real-time performance.

The application discloses an infrared image target tracking method based on a kernel correlation filter, which comprises the following steps: S1, extracting features of a target region to obtain a plurality of feature points and a descriptor of the target; S2, training and updating filter parameters according to the target features; S3, calculating a response map of a search region by using the parameters in step S2; S4, judging whether the tracking result is valid according to an APCE index of the response map in step S3, if yes, outputting the result and returning to step S1, if not, executing step S5; S5, matching the feature points of a current frame image with the target feature points, if the matching is successful, calculating the target position and size and outputting the same, and then returning to step S1, otherwise, reading a new frame image and executing step S5 again. The application can avoid parameter degradation when the target is seriously occluded or beyond the field of view, and re-track the target after the target appears.

An audio apparatus comprises a receiver (201) arranged to receive a set of input audio signals. An audio beamformer (101) performs beamforming by combining outputs of filters. A feedback circuit (103) comprising a matching set of filters with complex conjugate frequency responses and a beamform adapter (105) adapts the filters in response to a comparison of the input audio signals and the feedback audio signals. An adaptive coefficient processor (207) determines decorrelation coefficients for a set of spatial decorrelation filters generating decorrelated output signals from the input audio signals where the decorrelation coefficients are adapted based on the input signals. A set of spatial filters (205, 301, 401) are arranged to apply a spatial filtering to the input audio signals or the feedback signals where the set of spatial filters have coefficients that are determined from the decorrelation coefficients.