41results about How to "Avoid Tracking Failures" patented technology

The invention particularly discloses a contour gradient-based target tracking method. The method comprises the steps of segmenting candidate targets in an initial frame of an input video or image sequence, and extracting a reference image; extracting a contour of the reference image to serve as a standard feature template, and performing scale and angle conversion to obtain a multi-scale and multi-angle template sequence; from the next frame of the input video or image sequence, extracting gradient features of a target image; performing sliding window scanning based on a step length of 1 for the multi-scale and multi-angle template sequence on a feature image of the target image, and calculating the similarity of matching between the multi-scale and multi-angle template sequence and the feature image; according to information such as a matching position, a scale factor, an angle factor and the like, segmenting out a sub-region where a target is located in the target image to serve as areference image for the next detection to perform update; and repeating the steps until all the frames of the video or image sequence are detected. The invention aims to provide the contour gradient-based target tracking method for adapting to scale change and/or angle change of the target and improving target tracking accuracy.

The embodiment of the invention discloses a pose determining method and device and a storage medium, and belongs to the technical field of computers. The method comprises: based on the homography matrix of a marker image relative to a specified coordinate system, tracking the feature points of the marker image and acquiring the homography matrix of a specified image relative to the specified coordinate system; and decomposing the homography matrixes according to a constraint condition and a depth hypothesis condition to obtain a rotational displacement matrix of the specified image relative tothe marker image; and acquiring the pose parameters of the specified image relative to the marker image from the rotational displacement matrix. When the homography matrixes are decomposed, the influence of the depths of the feature points on a tracking process is taken into consideration, and the pose parameters of the specified image relative to the marker image are obtained under the same depth of the feature points on the horizontal plane of the specified coordinate system, thereby determining the position and the attitude of a camera and improving the tracking accuracy of the camera. Noerror is introduced even when switching the marker images.

The invention relates to a long-time sheltering robust tracking method based on convolutional features and global search detection. Through adopting a convolutional feature and multi-scale relative filtering method in a tracking module, the feature representation capacity of a tracking object appearance model is enhanced, so that a tracking result is very robust to factors such as illumination variation, object scale variation and object rotation. In addition, through the introduced global search detection mechanism, a detection module can detect the object again when the tracking fails because the object is sheltered for a long time, thus the tracking module can recover from error, and the object can be continuously tracked for a long time even in the case of object appearance changes.

The invention provides a face tracking method based on a multiple appearance models. The method comprises the steps of face tracker start-up, tracking detection, multiple appearance model building, appearance similarity score calculation and face location based on weighted fractional level fusion. During the process, a variety of appearance models are used for matching in different tracking scenarios; a long-term facial appearance feature update relationship diagram is used for learning; and sub-images of the same structure are used for face location. The invention provides a face tracker which uses multiple appearance models for face tracking, wherein a face detector assists the face tracker in face location during short-term appearance changes and reinitialization. A face position with the highest fusion score is selected as a candidate, and the final tracking output is acquired. The problems of obstacle obstruction, scale change and the like in the tracking process are effectively solved. The face tracking method makes further contributions to new design in engineering field and innovative solutions in monitoring field.

The invention discloses a target tracking method and a device based on targeted candidate areas, wherein the target tracking method comprises: determining the current frame image containing a tracked target; obtaining the tracked target area from the current frame image; obtaining the frame image following the current frame; obtaining a plurality of target candidate areas from the following frame image; calculating the similarities between the tracked target area and each target candidate area; based on the similarities, determining the target tracking area from the multiple target candidate areas. According to the invention, through the target candidate manner, the specific position of a tracked target can be determined; and in the tracking process, the tracked target object can be detected accurately, therefore, effectively increasing the stability of tracking a target object and avoiding the tracking failure.

The invention relates to a multi-camera combined initiative object tracking method for teleoperation of a mechanical arm, which belongs to the field of the teleoperation of the mechanical arm. The method comprises the steps of installing a plurality of cameras in different angles on the basis of the teleoperation, calibrating each camera, realizing the combined initiative tracking by adopting the matching of particle filter and sift local characteristics, enabling a to-be-tracked target to always stay in the center of a view field, and monitoring each angle of the mechanical arm, so that the tracking failure caused by factors such as the shielding can be avoided, and the tracking robustness can be improved by adding the human-machine interaction. According to the method, in the teleoperation process, a new target is marked in a human-machine interaction way, so that the target always stays in an observation area of an operator, or the target is updated, so that a subsequent control strategy can be made by the operator.

The invention provides a real-time detection tracking frame and tracking method based on compressed sensing feature selection. According to the tracking frame and tracking method, compressed features can be selected, and only the sample characteristic with a high distinction degree is used for classification. Real-time tracking can be achieved through the frame and method, the tracking failure phenomenon caused by selection of wrong features is avoided, the influence of bad features on the tracking result is effectively restrained, the tracking speed is obviously increased, and the tracking precision is obviously improved.

The invention discloses a target tracking method based on a conditional adversarial generation twinning network. The method comprises the following steps: (1) acquiring real-time video data for data preprocessing; (2) inputting the data preprocessed in the step (1) into a conditional adversarial generation twin tracking network consisting of a conditional adversarial generation deblurring network module and a full-convolution twin tracking network; and (3) embedding the conditional adversarial generation deblurring network module subjected to adversarial optimization into the full-convolution twinning tracking network framework subjected to feedback updating, and performing target tracking on the preprocessed real-time video data by using a conditional adversarial generation twinning tracking network subjected to online combination to obtain a target position. The dynamic adjustment capability of the tracking network is enhanced, the robustness of a tracker is improved, the tracking network is subjected to separation training in a transfer learning mode, the over-fitting phenomenon is prevented, the training time is shortened, and the generalization capability of the tracking network is improved.

The invention relates to a robot positioning and mapping method and device based on point-line feature fusion, a computer device and a storage medium. The method comprises the following steps; using an RGB-D camera for carrying out surrounding environment detection; acquiring an RGB image and a depth image; continuous image frames are determined; extracting point features from continuous image frames; using a self-adaptive line segment detection method; extracting line features from the continuous image frames; using a feature matching algorithm and a screening mechanism; performing feature matching on the line features; obtaining an inter-frame initial pose; and finally, carrying out minimum error processing on the inter-frame initial pose by adopting a dot-line error model, to obtain theinter-frame pose and the map point, and generating the local map based on the inter-frame pose and the map point, so that the tracking robustness is improved through point-line feature fusion. The problem of tracking failure in scenes such as low texture and motion blur is avoided, and the positioning and mapping accuracy of the robot is effectively improved.

A multi-feature and self-adaptive standard hedging combined target tracking method is characterized by comprising the following steps that (1) using multiple features of a tracking target to constructa dictionary, wherein the tracking target is expressed as a vector in a dictionary space; (2) designing self-adaptive standard hedging based on the standard hedging; and (3) in a self-adaptive standard hedging tracking framework, estimating the current tracking target. The multi-feature and self-adaptive standard hedging combined target tracking method still has good accuracy and robustness in acomplex environment.

The invention discloses an integrated environment perception and multi-target tracking system. The system comprises an image acquisition system, which is used for acquiring images around a maneuvering target in real time for preprocessing and sending the images to an image splicing projection system and a target identification tracking system; an image splicing projection system, which is used for splicing the received images and performing 3D projection to form a 3D panoramic image and sending the 3D panoramic image to the display control system; a target recognition and tracking system, which is used for recognizing sensitive targets from the received images, carrying out frame selection on the recognized sensitive targets and outputting the selected sensitive targets to the 3D panoramic image in a superposed manner, and is used for carrying out continuous recognition and tracking on the recognized sensitive targets, locking the selected specific targets and carrying out continuous recognition and tracking; and a display control system which is used for displaying the 3D panoramic image and used for selecting a specific target in a touch control mode. The system environment sensing distance can be effectively improved, the visual field of maneuvering target passengers is expanded, and the capacity of the passengers for recognizing the field complex environment is comprehensively improved.

The invention provides a method and device for tracking the head part of strip steel. The method comprises the steps: if it is determined that a current identity value corresponding to a shearing mark position of a flying shear is a valid identity value, acquiring first actual distance and first tracking distance of operation of the strip steel; if it is determined that a first difference value between the first actual distance and the first tracking distance meets a preset distance interval, generating a first strip steel head part tracking signal; when it is determined that a detection signal of a metal detector changes from an invalid identity value to the valid identity value, acquiring second actual distance and second tracking distance of the operation of the strip steel; if it is determined that a second difference value between the second actual distance and the second tracking distance does not meet the preset distance interval, giving up generation of a second strip steel head part tracking signal; and tracking the head part of the strip steel according to the first strip steel head part tracking signal. In such a manner, even if the head part of the strip steel cannot be tracked through the detection signal, the head part of the strip steel is triggered and tracked according to the second strip steel head part tracking signal, and the head part of the strip steel is accurately identified, so that a steel-heaping accident is avoided, and the production efficiency is guaranteed.