44results about How to "Tracking results are accurate" patented technology

The invention discloses an improved target tracking method, and solves the technical problems in the prior art that a Mean-shift algorithm tracking window is fixed and Kalman prediction is sensitive in target adhesion. The specific method includes: using Kalman filtering to predict a target position, reducing a research range, then matching a target through binary image features, correcting kernel function bandwidth of a Mean-shift algorithm, updating the Kalman model, searching a matched target in the vicinity of the predicted position, and completing target tracking by Mean-shift vector iteration. The improved target tracking method provided by the invention combines the advantages of Kalman prediction and the Mean-shift algorithm, is small in calculated amount and has good real-time performance, and effectively guarantees tracking accuracy under a circumstance of target adhesion.

The invention relates to a real-time tracking method of a nonspecific target based on partitioning, comprising three steps of classifier updating, target detection and weight updating. In the method, the target region is divided into multiple blocks; a classifier is used for maintaining each block, and updating is conducted frame by frame; the detection result of each classifier is comprehensively considered to determine the position of the target in the new video frame. In the method, an automatic weight updating mechanism is designed, so as to enable the blocks which are relatively stable to have greater decision-making power over the judgment of results, thus reducing the influence of various interferences; and the tracking performance is better than multiple international published algorithms recently. In the method, changes of appearances of objects caused by various interferences can be captured and accurate tracking can be conducted; the method has universality on target objects in various shapes and types; the calculation has low complexity and can be processed at real time. The invention has wide application prospect in various occasions needing tracking techniques, such as video monitoring, automatic driving, man-machine interaction, intelligent traffic, robot, airborne early warning and the like.

The invention provides a multi-human-body tracking method based on a human body part model and relates to the video monitoring technology field. The method comprises the following steps of acquiring image data, extracting the human body part model of each person and calculating a color characteristic, acquiring the part characteristic set of each person, and gathering and acquiring a multi-human-body part characteristic set list; then, calculating the similarity of the part characteristic set of each person acquired from a current frame and each person in the multi-human-body part characteristic set list acquired from a previous frame and acquiring a matching matrix; and according to the matching matrix, calculating the similar confidence of each person acquired from the current frame to each person acquired from the previous frame, according to the similarity and the similar confidence, matching a target acquired from the current frame with the target of the previous frame, and acquiring a tracking result. By using the method, the detection target of the current frame can be accurately matched to the position of the previous frame and a new target is discovered, and the problem oftracking failure caused by a characteristic difference generated because of human body deformation during a multi-human-body tracking process is effectively solved.

The invention discloses a multi-sensor target fusion and tracking method and system. The fusion method comprises the steps of firstly judging whether data transmission time of two sensors is valid ornot, then performing time and space registration on the two pieces of data which are judged to be valid, and removing objects which should not participate in target matching fusion; calculating the fusion coefficient of the data transmitted by the two sensors, and calculating the optimal matching according to the obtained fusion coefficient. According to the tracking method, on the basis of the fusion method, the tracked target state is updated according to the optimal matching result, the tracking result with the large error is screened and processed according to the tracking historical result and prior information, and finally high-precision detection and tracking of the dynamic obstacle are achieved.

The invention discloses a multi-target tracking method based on multi-agent deep enhancement learning. The method comprises: detecting multiple targets through a target detector, regarding the detected multiple targets as multiple agents, and then using a deep enhancement learning method to obtain a combined action set of the multiple targets, thereby completing multi-target tracking. The method applies a multi-agent deep enhancement learning technology to the multi-target tracking method for the first time, and can overcome technical disadvantages that artificial design features are not comprehensive and not accurate enough, and can improve calculation speed, realize real-time tracking. The method has high multi-target tracking accuracy rate and precision, and the number of misinformationand missing reports is low. The method is less affected by interference factors in a multi-target tracking scene, and tracking results are more accurate.

The invention provides a target object tracking method and device. The method comprises the steps: extracting marked parts of a target object detected in a current frame parallax image; carrying out the clustering of parallax points in a next frame parallax image, so as to form a plurality of cluster; calculating the similarity of each cluster in the next frame parallax image with each marked part, so as to determined candidate clusters similar to the marked parts; carrying out the grouping of all candidate clusters according to the position relation among all marked parts, so as to enable the candidate clusters belonging to the same object to be divided into the same group; determining the similarity of each group with the target object based on the similarity of all candidate clusters in each group; selecting the group with the highest similarity, and enabling the position where the group is located to serve as the position where the target object is located in the next frame parallax image. Through the technology of target object tracking, the method also can obtain an accurate tracking result under the conditions that the posture of the target object is frequency changed and the target object is partly shaded.

The embodiment of the invention provides an object tracking method and a device, and the method comprises the steps: obtaining a plurality of frames of first images photographed by a first photographing device and the first photographing moment of each frame of first image, and the first image comprises a first object; obtaining a plurality of frames of second images shot by a second camera deviceand a second shooting moment of each frame of second image, wherein the second image comprises a second object; obtaining the distance between the first camera device and the second camera device; and judging whether the first object and the second object are the same object according to the multiple frames of first images, the first shooting moment of each frame of first image, the multiple frames of second images, the second shooting moment of each frame of second image and the distance. According to the object tracking method and the device provided by the embodiment of the invention, theproblem that the robustness of the existing scheme is poor due to the fact that the existing scheme is greatly influenced by the image shooting angle can be solved, and the tracking result is more accurate.

The invention belongs to the object tracking control field, and discloses a two-dimension track tracking controller of the underwater robot. The two-dimension track tracking controller is realized through steps of utilizing a least square method to perform modeling on a movement track under a motion coordinate system, designing a self-adapting wide domain prediction control two-dimension track accuracy tracking controller of the underwater self-adapting robot on the basis of that, and realizing the two-dimension track tracking control of the underwater robot. The invention is applicable to the object tracking and identification.

Embodiments of the invention provide a channel tracking method and device, and relates to the field of communication channel processing. The method and system can perform channel tracking on communication systems provided with uniform rectangular arrays. The method includes obtaining an initial emission horizontal angle, an initial emission pitch angle, an initial receiving horizontal angle, an initial receiving pitch angle and preset gain information; calculating the desired value of a channel matrix of a current time block on the basis of a preset channel matrix formula and channel time-varying formula according to the obtained information; calculating the estimated values of a horizontal and vertical array of the current time block according to the obtained information, a preset angle time-varying formula and the preset channel matrix formula; obtaining the optimal value of the horizontal and vertical array of the current time block on the basis of a matrix decomposition algorithm according to the estimated values of the horizontal and vertical array of the current time block and the desired value of the channel matrix of the current time block; and calculating the optimal valueof the channel matrix of the current time block according to the optimal value of the horizontal and vertical array of the current time block.

The invention relates to a K sparseness based rapid robust target tracking method. The K sparseness based rapid robust target tracking method comprises the following steps: solving a sparse matrix byutilizing a positive and negative template dictionary; solving an optimization problem of the positive and negative template dictionary l1 by utilizing the sparse matrix, namely K sparseness, whereinunder the condition that the sparseness is relatively small and the signal-to-noise ratio is the same, the speed of solving a sparse coefficient is 10 times or more than 10 times of an LASSO (Least Absolute Shrinkage and Selection Operator) algorithm; dividing a current-frame image into a plurality of image samples and calculating a reconstruction error by utilizing a normalized positive templatematrix and a normalized negative template matrix; judging error parameters through reconstruction and comparing samples obtained by histograms, so as to realize target tracking in the current-frame image. Therefore, the target tracking accuracy is also improved, a more accurate tracking result can be obtained and the instantaneity of tracking is improved.

The invention discloses a moving target tracking method based on a cascade detector. The moving target tracking method comprises the following steps of (1) initializing a related filtering tracker; (2) initializing a cascade detector; (3) enabling the correlation filtering tracker to obtain a first region of interest; (4) detecting a cascade classifier target; (5) synthesizing the first region ofinterest and the detection region; (6) updating the tracking model and the cascade detector, wherein the first region of interest is obtained through the correlation filtering tracker, the cascade detector comprises a variance classifier, a multi-channel random fern classifier and a related consistent classifier, the detection area is acquired through the three classifiers, the first region of interest and the detection area are subjected to weighted correction to acquire the second region of interest, so that the oscillation of a tracking result can be effectively prevented. Meanwhile, the accuracy of the first region of interest acquired by the tracking model can be ensured by adopting the cascade detector.

The invention discloses an object tracking method and device, and relates to the field of image processing. According to the specific implementation scheme, multiple frames of first images shot by a first camera device and a first shooting moment of each frame of first image are acquired, wherein the first images comprise a first object; multiple frames of second images shot by a second camera device and a second shooting moment of each frame of second image are obtained, wherein the second image comprises a second object; a relative relationship between the first camera device and the secondcamera device is obtained; and whether the first object and the second object are the same object or not is judged according to the multiple frames of first images, the first shooting moment of each frame of first image, the multiple frames of second images, the second shooting moment of each frame of second image and the relative relationship. According to the object tracking method and device provided by the embodiment of the invention, the problem that the robustness of the existing scheme is poor due to the fact that the existing scheme is greatly influenced by the image shooting angle canbe solved, and the tracking result is more accurate.

The invention discloses an echo block tracking method and device of a radar echo image and a storage medium, and relates to the technical field of weather forecast, and the echo block tracking method of the radar echo image comprises the following steps: obtaining a plurality of radar echo images to form a radar echo image sequence, wherein a plurality of radar echo images in the radar echo image sequence come from the same radar and are arranged according to a time sequence; performing hierarchical division on each radar echo image in the radar echo image sequence to obtain a plurality of binarized hierarchical matrixes; determining all echo blocks in the hierarchical matrix according to a communication rule; calculating the similarity between echo blocks in the hierarchy matrixes of the same hierarchy of two adjacent radar echo images; and judging the two echo blocks with the similarity greater than a threshold as the same cloud cluster. By comparing the similarity of the echo blocks hierarchically, the echo blocks in the radar echo image can be accurately tracked.

The present invention provides a multi-target tracking algorithm based on multilayer restriction. The multi-target tracking algorithm comprises: employing a simple strategy to rapidly obtain tracking segments, employing vision information to perform correction and segmentation of all the tracking segments, converting a result to a graph model about the tracking segments for solution, employing high-level semantic information of the result obtained through solution to perform further correction, and obtaining a final tracking target track. The multilayer restriction multi-target tracking algorithm fully employs the vision feature of a tracking target to perform restriction layer by layer from shallow to deep so as to solve the problem that an accurate result is difficult to obtain because a current method is difficult to fully employ the vision feature and allow the tracking result to be accurate.

The invention discloses a blended ore tracking judgment method and system. The method comprises the steps that: whether a material bin starts feeding or not is judged; if yes, the current time is recorded, and the material bin starting feeding is set as a feeding bin; the feeding duration of the feeding bin is calculated; and the time obtained by subtracting the feeding duration from the current time is taken as the actual feeding time of the uniformly mixed ore. The feeding starting time of the material bin is detected and judged, the detected feeding starting time of the material bin subtracts the time after the feeding duration time to serve as the actual feeding time of the blended ore, the tracking result of the new blended ore component feeding time is basically not delayed, the tracking result is more accurate, and material batching is more accurate.