35results about How to "Improve tracking robustness" patented technology

The invention provides a multi-target tracking method integrating obvious characteristics and block division templates. A target motion area is detected by adoption of RGB component background difference and an iterative threshold, and the adaptive ability of a motion detection algorithm to scene illumination change is improved. Based on target area block division, a motion pixel color saliency weighted block centroid model, block centroid shifting fusion and a scale updating method, the calculation efficiency is high, the resistance to partial occlusion is high, and the similar color scene jamming ability is strong. The problem of multi-target measuring-tracking distribution is solved by adoption of two-level data association, and an occluded local area can be accurately positioned. Therefore, adaptive template updating is guided by an occlusion matrix, a reliable global centroid transfer vector is obtained by making use of effective colors and motion information of blocks, and finally, continuous, stable and fast multi-target tracking in complex scenes is realized. The multi-target tracking method integrating obvious characteristics and block division templates is applied to fields like intelligent video surveillance, in-air multi-target tracking and attacking, and multi-task tracking intelligent robots.

The invention relates to a contour tracing method based on shape-transmitting united division and image-matching correction. The contour tracing method comprises the steps of in a shape-transmitting united division part, combining a shape prediction image with original image and video frames, by virtue of relocation of position coordinates, providing a new image model construction method, constructing a new energy optimization function to carry out united division on an image model, in an image-matching correction part, carrying out image matching on a division result of a current image with a division result of a previous frame image, when the result of the image matching can not meet a limiting condition, regulating weighting parameters of an energy function in the shape-transmitting united division, and correcting inaccuracy of the division result. According to the contour tracing method based on the shape-transmitting united division and the image-matching correction, an accuracy rate of contour tracing of a target object in a video can be effectively improved.

The invention discloses a target tracking method based on correlation of space-time-domain edge and color feature. The target tracking method based on correlation of space-time-domain edge and color feature comprises the following steps: (1) selecting a tracked target area; (2) extracting the edge outline of the target and calculating the direction angle of the edge; (3) along the two orthogonal directions of horizontal direction and vertical direction, conducting statistics of edge-color symbiosis character pairs, and building a target edge-color correlation centroid model; (4) selecting the centroids of the edge-color pairs with high confidence coefficient to conduct probability weighting, so as to gain a transfer vector of a target centroids in a current frame; (5) conducting statistics of histograms of target edge distances between adjacent frames, conducting probability weighting of the successfully matched distance change rates between the adjacent frames so as to gain a target dimension scaling parameter. By means of the target tracking method based on correlation of space-time-domain edge and color feature, a target tracking in a crowded scene, a shelter, and a condition that the target dimension changes is achieved, and robustness, accuracy and instantaneity of the tracking are improved. The target tracking method based on correlation of space-time-domain edge and color feature has a wide application prospect in the video image processing field, and can be applied to the fields such as intelligent video monitoring, enterprise production automation and intelligent robot.

The invention discloses a particle filter infrared tracking method that combines gradient features and self-adaptive templates. The steps are as follows: first, the particle filter algorithm is used as the main frame of the infrared image tracking algorithm; The gradient histogram of the target is used as the target observation model; again, the adaptive template update strategy is integrated in the tracking algorithm to update the target feature template; finally, in order to improve the real-time performance of the algorithm, the tracking program is parallelized with the help of a shared memory parallel programming library. The invention can ensure long-term stable and precise tracking of infrared targets, has high computing efficiency, and can provide theoretical support for infrared guidance engineering.

The invention discloses a stable feature mining and target tracking method based on clustering sub-area association. Residual peak energy to obtain the number of clusters; b) construct the S and V component sample grayscale matrix of the target motion area, and perform class-adaptive K-means clustering; c) label and establish the class-based connected sub-regions Sub-region template, observation model and incremental model description; d) Establish the correlation between the target template and the current observation model sub-region, and mine the "template-observation" stable sub-region feature pair and template feature change rate; e) Weighted fusion template for each stable Sub-area displacement, target detection area center and previous frame trajectory to locate the target current trajectory, and update target template frame by frame according to the weighted average increment of stable features and their rate of change.

The invention provides a multi-target tracking method integrating obvious characteristics and block division templates. A target motion area is detected by adoption of RGB component background difference and an iterative threshold, and the adaptive ability of a motion detection algorithm to scene illumination change is improved. Based on target area block division, a motion pixel color saliency weighted block centroid model, block centroid shifting fusion and a scale updating method, the calculation efficiency is high, the resistance to partial occlusion is high, and the similar color scene jamming ability is strong. The problem of multi-target measuring-tracking distribution is solved by adoption of two-level data association, and an occluded local area can be accurately positioned. Therefore, adaptive template updating is guided by an occlusion matrix, a reliable global centroid transfer vector is obtained by making use of effective colors and motion information of blocks, and finally, continuous, stable and fast multi-target tracking in complex scenes is realized. The multi-target tracking method integrating obvious characteristics and block division templates is applied to fields like intelligent video surveillance, in-air multi-target tracking and attacking, and multi-task tracking intelligent robots.

The invention provides a multiwindow-based target tracking method, belonging to the computer vision technology. The method comprises the following steps: 1) reading in the image of the initial position of the target, constructing N-numbered tracking windows according to the target characteristics and measuring the compensation di between each tracker and the initial position of the target X0=(x0, y0); 2) reading in the next frame image and computing the new position Yi of each tracker on the current image frame; 3) computing the weight Wi of all the trackers; 4) compensating the new positions Yi of all the trackers on the current image frame by the compensation di and obtaining the value shown in the specification; 5) computing the target position parameter E(X) with the weight Wi of the trackers and the value shown in the specification; and 6) initializing all the trackers according to the target position parameter, updating the reference windows of the trackers and continuously reading in the next frame image, wherein each window corresponds to one tracker. The tracking method can improve the tracking performance of the system under the complex conditions of shading, lighting variation, etc and has strong robustness, and the target tracking result is free from control by the result of a certain tracking window.

The invention relates to the field of three-dimensional reconstruction, in particular to a quick and robust RGB-D indoor three-dimensional scene reconstruction method, and aims to solve the problem that the indoor three-dimensional scene reconstruction efficiency cannot meet the demands. The method comprises the steps of scanning an indoor scene in real time by adopting an RGB-D camera; performingcamera real-time tracking based on a point line fusion camera tracking algorithm; performing reduction on camera tracks by detecting a camera state; and performing multi-scale voxel data fusion on anRGB-D image by utilizing reduced camera track information to generate a complete three-dimensional scene model. The complete indoor scene model can be obtained efficiently and accurately. A system has good robustness and expansibility.

The invention discloses a time-space perception-based correlation filtering target tracking method, which relates to the field of image processing target tracking. This method follows the idea of ​​reading the target image, extracting target features, training filter templates, extracting target multi-scale features, and determining the scale and position in the new image of the target to track the target. The structural framework and target features of the correlation filtering target tracking algorithm The extraction method has been optimized and improved, which makes this method have obvious advantages compared with traditional algorithms in terms of tracking robustness and accuracy. This method can enhance the robustness and accuracy of correlation filtering target tracking, solve the model drift problem caused by linear update of the template, improve the long-term tracking effect of correlation filtering target tracking, and at the same time ensure the real-time performance of target tracking. An important improvement in the.

The invention discloses an online target tracking method combining with feature point matching, and the method comprises the steps: firstly calculating a salient region of a tracked region, thereby obtaining an optimal target tracking region; secondly employing a median flow method for tracking, employing a method combining with the feature point matching for estimating the scale change of a target, and deleting interference points through employing a layer clustering method, and obtaining a final position of the target; finally feeding back the scale information estimated by a tracker to a detector, and increasing the speed of the detector through reducing the size of a detection scale space. The method employs a target tracking method based on TLD, carries out the target tracking through employing the significance of the tracked target, and combines with an optical flow method and the feature point matching method so as to improve the target tracking robustness of a tracking algorithm in different scenes. The method is simple and easy, and is high in implementation speed.

The invention relates to a texture-free three-dimensional object tracking method based on confidence coefficient and feature fusion. The tracking method comprises the following steps: (1) establishinga color model; (2) dividing the pixel points into contour points and region points by using a cluster structure; (3) determining the weight alpha i of the edge item, the weight beta i of the color item and the cluster weight omega i according to the confidence coefficient of the contour point and the confidence coefficient of the region point; (4) solving an optimal pose according to the total energy equation corresponding to all the clusters, and rendering the three-dimensional model of the object to obtain an object area on the current frame image; and (5) repeating the steps until the tracking is finished. According to the method, a clustering structure is used, contour points and regional points are re-unified into one energy function, and the problem of non-uniform sampling points issolved; the confidence coefficients of the edge points and the region points are calculated respectively, the edge points and the region points are normalized automatically, the weight of each energyitem is calculated according to the confidence coefficients, and the problem of non-uniform error measurement of different features is solved.

The invention discloses a GNSS carrier tracking method based on Doppler residual error estimation and a receiver. The receiver includes a receiving antenna, a radio frequency front-end module, a baseband signal processing module, a positioning solution module, and a tracking parameter prediction module. The tracking method includes: the radio frequency front-end module receives the GNSS signal transmitted by the receiving antenna and processes the GNSS signal to obtain a digital intermediate frequency signal; the baseband signal processing module captures and tracks the digital intermediate frequency signal to obtain tracking data; The calculation module obtains positioning information according to the tracking data; the tracking parameter prediction module obtains Doppler prediction results; the baseband signal processing module uses Doppler residual estimation to perform carrier signal tracking in the next epoch. The invention can expand the traction range of the carrier tracking error, enhance the adaptability of the loop to dynamic signals, and effectively improve the tracking robustness of GNSS reception in high dynamic scenes.

The invention relates to a contour tracing method based on shape-transmitting united division and image-matching correction. The contour tracing method comprises the steps of in a shape-transmitting united division part, combining a shape prediction image with original image and video frames, by virtue of relocation of position coordinates, providing a new image model construction method, constructing a new energy optimization function to carry out united division on an image model, in an image-matching correction part, carrying out image matching on a division result of a current image with a division result of a previous frame image, when the result of the image matching can not meet a limiting condition, regulating weighting parameters of an energy function in the shape-transmitting united division, and correcting inaccuracy of the division result. According to the contour tracing method based on the shape-transmitting united division and the image-matching correction, an accuracy rate of contour tracing of a target object in a video can be effectively improved.

The invention discloses a method for extracting and analyzing the motion trajectory of multiple pigs in a group environment, comprising the following steps: improving a known single-target tracking algorithm, and proposing an algorithm for simultaneously tracking multiple pigs in a group environment; The coordinates of the center point of each frame of each pig draw the movement trajectory and instantaneous speed of each pig; according to the target frame obtained by the tracking of the pig body, extract the region of interest, segment the target outline of each pig, and judge the pig body The current morphological feature is standing or lying down; combined with the movement track, movement speed, and contour shape of the pig body, combined with the movement law of the pig body and expert advice, the movement behavior of each pig in each time period is judged, mainly including: pig body standing, Lying prone, walking slowly, running fast, abnormal restlessness. The invention shows how to monitor the behavior of pigs in a group environment by using an accurate and robust tracking algorithm, which can detect abnormal movements of pigs in real time and assist in judging the health status of pigs.