9 results about "Correlation filter" patented technology

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 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.

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

This invention discloses a robust tracking method for multiple moving targets using through-wall radar based on multi-domain data association and multi-state trajectory management. First, radar measurement results are obtained using a constant false alarm rate (CFAR) detection method. Second, the Euclidean distance between the measurements and the trajectories, the kernel correlation filter response, and the trajectory confidence cost are calculated to construct a minimum-cost maximum flow problem for data association. Subsequently, a 5-state trajectory management mechanism is established. Based on the target state and association results, new trajectories are generated, regular and unmatched trajectories are updated, and cross-trajectories are generated, updated, and separated. Specifically, in the cross-trajectory separation process, Bézier curves are used to fit the cross-trajectories, and the target state is estimated based on the Bézier curves to predict the target's position in the next frame. The re-matching of cross-trajectories and measurements is achieved based on the distance between the predicted position and the measurements.

The application discloses a target tracking method, system and device based on a multi-level loss recovery mechanism, comprising the following steps: collecting a video frame sequence and initial target region information; using a kernel correlation filter to perform target tracking on a current video frame, extracting HOG features of the target and generating a response map; using a Kalman filter to correct the motion state of the tracking result of the kernel correlation filter and outputting a corrected target position; evaluating the tracking confidence of the current target, and when the reliability evaluation result is lower than a preset threshold, starting a backup template mechanism for secondary verification; when a plurality of backup templates in succession fail, triggering a target loss redetection mechanism to redetect the target, obtaining a candidate region of the target, and performing multi-scale search and correction on the redetected candidate region to recover normal tracking of the target. The application can realize high-robustness and real-time embedded target tracking.

The present application belongs to the technical field of data processing, and discloses a kind of airborne terahertz video synthetic aperture radar data real-time processing method.The present application uses the image registration method based on kernel correlation filtering to estimate and correct the relative shift between image sequences;Secondly, the motion target shadow is detected using a deep neural network;Then, the detection results are used as the input of the improved SORT algorithm to track the motion target;Finally, the latitude and longitude coordinates of the motion target are obtained based on the pixel position and latitude and longitude coordinates of the auxiliary reference point.The present application fills the technical gap of real-time data processing methods that match the imaging rate of airborne terahertz video synthetic aperture radar in practical applications.

In various examples, image areas may be extracted from a batch of one or more images and may be scaled, in batch, to one or more template sizes. Where the image areas include search regions used for localization of objects, the scaled search regions may be loaded into Graphics Processing Unit (GPU) memory and processed in parallel for localization. Similarly, where image areas are used for filter updates, the scaled image areas may be loaded into GPU memory and processed in parallel for filter updates. The image areas may be batched from any number of images and / or from any number of single-and / or multi-object trackers. Further aspects of the disclosure provide approaches for associating locations using correlation response values, for learning correlation filters in object tracking based at least on focused windowing, and for learning correlation filters in object tracking based at least on occlusion maps.

The audio device includes a receiver 201 configured to receive a set of input audio signals. An audio beamformer 101 performs beamforming by combining the outputs of filters. A feedback circuit 103 includes a set of matching filters having a complex conjugate frequency response, and a beamform adapter 105 adapts the filters in accordance with a comparison between the input audio signal and the feedback audio signal. A fitting coefficient processor 207 determines decorrelation coefficients for a set of spatial decorrelation filters that produce a decorrelated output signal from the input audio signal, and the decorrelation coefficients are adapted based on the input signal. Sets of spatial filters 205, 301, and 401 are configured to apply spatial filtering to the input audio signal or the feedback signal, and the sets of spatial filters have coefficients determined from the decorrelation coefficients.