60 results about "Correlation filter" patented technology

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.

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.

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.

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 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 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 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 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.

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 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.

The invention provides a piping monitoring system based on a sound wave frequency division technology, and the system comprises a self-correlation filtering module which is used for carrying out the self-correlation calculation of a sound wave signal containing piping state information, so as to filter an interference signal, and obtaining the sound wave signal from which the interference signal is filtered; the time-frequency conversion module is used for performing time-frequency conversion on the sound wave signal of which the interference signal is filtered out to obtain a frequency spectrum sequence of the sound wave signal of which the interference signal is filtered out; the band-pass filtering module is used for acquiring a filtering frequency band lower limit and a filtering frequency band upper limit of the frequency spectrum sequence, and filtering the frequency spectrum sequence according to the filtering frequency band lower limit and the filtering frequency band upper limit; and the piping monitoring module is used for calculating the amplitude of the filtered frequency spectrum sequence, if the amplitude is increased, the piping diameter is increased, and if the amplitude is decreased, the piping diameter is reduced. According to the invention, through the multi-stage filtering processing, the influence caused by the change of environmental factors such as temperature and humidity can be effectively resisted.

The invention discloses an external display type endoscope real-time image electromagnetic interference detection device and method, and relates to the technical field of medical instrument detection. The device comprises a mechanical support unit, an image acquisition unit, a core processing unit and a display interaction unit, and can realize stable acquisition and processing of display screen images outside the endoscope. The method comprises the following steps: firstly, positioning and extracting an ROI region through a multi-channel multi-scale feature fusion strategy, and introducing multi-frame stability judgment to ensure accurate positioning; then constructing a non-interference reference; a kernel correlation filtering tracker is combined with a periodic realignment mechanism to realize ROI stable tracking; a fusion difference chart is obtained through channel-by-channel difference calculation and fusion, and an interference area is extracted after dynamic threshold judgment; and finally, analyzing interference characteristics, and realizing accurate classification of four types of interference of splash screen, stripes, brightness drift and blank screen / signal loss. The method is high in real-time performance, comprehensive in detection type, accurate in judgment and capable of guaranteeing the endoscope image quality.

In various examples, a technique for calculating feature correlations given a stereo image pair (acquired 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 calculating a corresponding set of correlation maps.The technique further includes: generating a set of compressed correlation maps; masking one or more portions of individual compressed correlation maps of 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 stereo image pair based at least on the set of masked correlation maps.

The invention relates to the technical field of audio signal processing, and provides an audio processing method and device, and the method comprises the steps: extracting late reverberation signals from a plurality of room impact responses, intercepting segments from each late reverberation signal, and constructing a decorrelation filter set; performing framing processing on the to-be-processed input audio signal to obtain a plurality of input audio frames; determining a decorrelation filter of each input audio frame from the decorrelation filter set, and filtering the corresponding input audio frame through the decorrelation filter of each input audio frame to obtain a decorrelation output frame of each input audio frame; and reconstructing each decorrelated output frame to obtain a continuous output audio signal. The phase characteristic of the late reverberation signal adopted by the invention has real and natural randomness, so that the problems of unnatural hearing feelings such as granular feelings and glitch feelings possibly introduced by a traditional frequency domain random phase method are avoided while the correlation between sound channels is effectively reduced and the sense of space and immersion are enhanced.

The application discloses a target tracking positioning method based on non-parametric classification and composite reliability, relates to the field of target tracking, and comprises the following steps: step 1, designing an underwater video target tracking positioning system based on an imaging sonar and a binocular camera; step 2, establishing an underwater moving target multi-mode detection method based on non-parametric pixel classification; step 3, constructing a correlation filter learning model based on composite reliability, containing spatial reliability and multi-feature multi-channel reliability; step 4, solving a correlation filter learning optimization problem based on composite reliability; and step 5, completing joint tracking positioning based on the binocular camera and the imaging sonar. The application can improve the reliability and effectiveness of underwater robots in unstructured environments, and provides theoretical and technical support for the development of underwater robots, deep sea exploration and space robots in China.

Simulated audio signals in a virtual environment are decorrelated by obtaining (102) a first set of decorrelation filters (eg. frequency-selective phase shifting all-pass filters as in Fig. 3), evalua

The present application relates to the field of medical image processing, and particularly relates to an end-to-end ophthalmic surgery positioning navigation method and device. First, a coding and decoding network is used to extract eyeball features, and a multi-class segmentation layer constructed according to the eyeball features is used for semantic segmentation to determine the center position of the eyeball, so as to construct a polar coordinate sampler, so that the real-time collected intraoperative image is directly projected under the polar coordinate, and the eyeball feature map, the semantic segmentation result map and the preoperative image are converted to the polar coordinate system. Under the polar coordinate system, the current image and the target image are registered to complete eyeball rotation tracking. In the registration process, a twin network is combined with a correlation filter, the tracking target position, number and receptive field size are defined by defining a Gaussian template, the calculation efficiency is improved, the weighted sum of the inter-frame registration result and the long-time registration result is taken as the final rotation amount, the cumulative error is eliminated, and the stability and registration accuracy of the eyeball rotation continuous registration are improved.

The application discloses a deep spatio-temporal correlation video target tracking method and system, which realizes target tracking for each video sequence and guarantees to obtain accurate video target tracking results. Firstly, a spatio-temporal feature extractor is designed to extract spatio-temporal features of a template sequence and a search sequence. Secondly, a feature matching module is introduced, which is composed of a classification branch and a regression branch. The extracted template spatio-temporal features and the search spatio-temporal features are subjected to similarity matching through correlation filtering to obtain multi-channel correlation filtering features respectively. Then, a target tracking module is deployed, which includes a classification head and a regression head, and the classification score map and the regression score map are calculated according to the input multi-channel correlation filtering features respectively, which are used to predict the target position and estimate the target scale. Finally, the spatio-temporal correlation visual tracking model is optimized by minimizing the defined joint loss. In the target tracking test, a confidence region estimation strategy is proposed, so that the target tracking is kept robust and accurate in the video sequence.

The invention discloses an anti-rotation correlation filtering tracking algorithm, and belongs to the technical field of computer vision target tracking, and the algorithm is based on an MOSSE framework, carries out the independent estimation of a target rotation angle through constructing a rotation angle filter, and achieves the precise tracking through combining with a position filter and a scale filter. Extracting a position training sample, constructing a scale pyramid and an angle pyramid, and respectively solving an initial position filter, a scale filter and an angle filter; during new frame tracking, position, angle and scale estimation is carried out in sequence, and the current state of the target is determined through the maximum value of the response diagram; meanwhile, model updating is supported, all filters are updated according to the learning rate, the algorithm specially optimizes a target rotation scene, the problem of tracking drift of a traditional algorithm is solved, the calculated amount is small, the real-time requirement can be met, and the algorithm is suitable for unmanned aerial vehicle nacelle, vehicle steering and other scenes.

The application provides a SAR deception jamming method of mixed domain efficient track deviation compensation, comprising the following steps: after a SAR radar enters a detection range of a jammer, relevant parameters of the SAR radar are acquired, and a jammer deception template is constructed according to false electromagnetic features generated in a SAR image by jamming; azimuth focusing and range migration compensation calculation are performed on the jammer deception template in an azimuth frequency domain, and then inverse Fourier transform in the azimuth direction is performed to obtain an initialization template; after the SAR radar enters a jamming area, real-time modulation is performed, track deviation compensation is performed on the initialization template after coupling term decomposition in the azimuth time domain, and a real-time SAR system correlation filter is constructed in the azimuth time domain pulse by pulse; the real-time SAR system correlation filter and the jammer correlation filter are multiplied in a range frequency domain to obtain a non-linear track jamming frequency domain response function, and convolution operation is performed on the non-linear track SAR deception jamming signal obtained after the SAR signal intercepted to obtain a non-linear track SAR deception jamming signal.

The application provides a small target tracking method and system based on improved correlation filtering, which comprises the following steps: cropping an initial target area and pre-processing an image, performing two-dimensional Fourier transform and making a correlation filtering tracking template, tracking in the target area, judging whether the target is lost according to the statistical result, inputting the tracking coordinates into a Kalman filter to predict the position of the next frame template, and updating the tracking template using a new target area. The method improves the original correlation filtering tracker by taking advantage of the feature of the Kalman filter that can predict the future position of the target. The system can improve the problems of target occlusion, too fast target motion, complex target background and the like in the small target tracking task under the premise of realizing high robustness of small target transformation.