10results about How to "Improve object tracking performance" patented technology

The invention relates to the technical field of space target monitoring, and discloses an RCS information and Doppler information-assisted adaptive PHD filtering method, which comprises the following steps of: 1, establishing a Gaussian mixture logarithmic normal distribution model; 2, on the basis of the Gaussian mixture logarithmic normal distribution model, improving the Gaussian component weight of the Gaussian mixture logarithmic normal distribution model, and establishing an adaptive PHD filter assisted by RCS information and Doppler information; according to the method, target RCS information and Doppler information are introduced into a multi-target filtering updating process, and target feature information is fully utilized to perform space multi-target tracking in a space-based scene; meanwhile, according to the method, the difference between the target and the clutter is enlarged by using the target RCS information and the Doppler information; the problems that an existing filtering method cannot provide target amplitude and RCS information, the accuracy cannot be effectively guaranteed, and different target tracks cannot be effectively distinguished are solved.

The invention discloses a relay tracking application method and system for large and medium-sized ships in open seas, and the method comprises the following steps: S1, obtaining the current position information of a target ship, and delimiting a circular region of interest based on the current position information; s2, performing overhead satellite calculation within half an hour based on the region of interest; the current position of the target ship is determined for uploading tasks to satellites passing through the top attention area in a short time; after receiving the AIS signal of the target ship, the micro-nano satellite transmits the AIS signal to the ground; and S3, selecting to enter a regular imaging tracking mode under the condition of stable AIS signal receiving or a search imaging tracking mode under the condition of short-time AIS signal loss according to the AIS signal receiving condition, and carrying out relay tracking on the target ship. The method has the advantages that the remote sensing image data transmission quantity for carrying out the tasks and the time consumed for completing the tasks can be greatly compressed, and the requirements of high-timeliness tasks such as high-sea far-field ship escorting and marine ship searching and rescuing are met.

The present invention discloses a twin network target tracking method combining template updating and trajectory prediction, including: obtaining a target tracking model through offline training of a preprocessed data set, extracting image features of the current frame through the target tracking model, and comparing it with the template image to determine the position information of the target to be tracked in the next frame in the image; during the entire target tracking process, the template image is updated online, and the template image features are continuously and iteratively updated; at the same time, according to the target motion position change information in the historical frames, the Kalman filtering method is used to predict the trajectory to achieve stable tracking of a single target.

The application discloses a multi-sensor cross prompting target tracking method and device, obtains working state information of all sensors, obtains a first number of sensors in a working state; compares the first number with a sensor working number threshold value, selects a sensor group according to a comparison result; obtains target detection information output by each sensor in the sensor group; fuses all target detection information to obtain target fused detection information; determines final target state information according to the target fused detection information; the application can coordinate and manage multiple sensors, avoids all sensors working at the same time, and can realize fast and accurate tracking of a target, improves working efficiency of the sensors; meanwhile, a weighted fusion method is adopted to fuse target detection information of the sensor group, more accurate final target state information is obtained, and the system has better target tracking performance.

The invention belongs to the field of radar signal processing, and discloses a joint resource scheduling and transmission zeroing method and system based on reinforcement learning. According to the method, in a scene that a plurality of transmitting nodes and a plurality of receiving nodes cooperatively execute a target tracking task in a receiving and transmitting separated MIMO radar network, aiming at the problem that a traditional radar resource allocation method lacks dynamic real-time adjustment capability and lacks comprehensive consideration on long-term performance of radar system resources, firstly, the dynamic real-time adjustment capability and the comprehensive consideration on the long-term performance of the radar system resources are considered; according to the method, joint optimization is carried out by considering transmission zero setting and resource scheduling, emission waveforms, residence time and power distribution of nodes are taken as optimization variables, then, cumulative signal-to-noise ratio constraints and the low interception probability of a whole task are considered at the same time, an optimization model for minimizing the interception probability is provided, and finally, for the optimization model, the optimization model is optimized. An algorithm based on reinforcement learning is adopted for solving, specifically, a PPO algorithm is adopted, and finally the low interception probability and the resource management efficiency are improved.

The invention provides a target tracking method and device based on visible light and ultraviolet light. The method comprises the following steps: acquiring a visible light modal image and an ultraviolet light modal image in real time; extracting a first template image covering a to-be-tracked target from the mth frame of visible light modal image, and extracting a second template image covering the to-be-tracked target from the mth frame of ultraviolet ray modal image; and inputting the first template image, the second template image, the nth frame of visible light modal image and the nth frame of ultraviolet ray modal image into a target tracking model for target tracking so as to obtain an nth frame of prediction result image, and marking the prediction position of the target to be tracked on the prediction result image. In the scheme, the target tracking model is used for processing the visible light modal image and the ultraviolet light modal image to track the to-be-tracked target, and even if the to-be-tracked target is mixed with the background color, the visible light and the ultraviolet light can be used for tracking the to-be-tracked target, so that the target tracking effect is improved, and the limitation of target tracking is reduced.

This invention discloses an improved unmanned surface vessel (USV) target tracking method: Step 1, acquire target USV position and heading angle data to obtain target USV sampling data; Step 2, based on the target USV sampling data, obtain heading angle change data at adjacent sampling times; Step 3, smooth the heading angle change data to obtain preprocessed data; Step 4, based on the preprocessed data and combined with the designed offset law, calculate the offset angle α; Step 5, calculate the absolute azimuth angle of the line segment connecting the USV and the target USV according to the target USV's heading angle and offset angle α; Step 6, based on the absolute azimuth angle and the target USV's position coordinate information (x... target ,y target ) Calculate the tracking point (x) under the relative time-varying point tracking strategy tracking ,y tracking The tracking point coordinates are obtained. This invention overcomes the problem of poor tracking performance when the target vessel turns, which exists in the tracking method that uses the center of the fan-ring domain as the tracking point. It also improves the target tracking effect and control stability, while reducing the energy consumption of the unmanned surface vessel.

The present invention relates to an anti-occlusion target tracking method based on attention mask, and belongs to the field of computer vision. In view of the existing tracking method, when the target is occluded, the problem of tracking drift is prone to occur. The method first extracts features of the candidate area image by adding a feature extraction network with an attention mechanism; then, using the AMGN network, according to the extracted candidate area features and the spatial attention matrix, occlusion feature samples are obtained to simulate various target occlusion situations in the feature space and enhance the occlusion positive samples; finally, using the discriminant network, according to the fusion of the occlusion feature samples of the second and third convolutional layers, the feature is distinguished as belonging to the target or the background, and finally the target tracking under occlusion is realized. The method is suitable for video surveillance, human-computer interaction, unmanned driving and other fields, and can accurately track moving targets.

The invention provides a target tracking method for fully mechanized coal mining face personnel and equipment based on a neural network and correlation filtering, and the method comprises the steps: calculating a gray difference value based on a previous frame of video image and a current frame of video image for a non-first frame of video image, and filtering the position information of a foreground target according to a gray threshold; obtaining a target position response diagram of a tracking target in the current frame video image according to the foreground target position information, the current frame video image and the trained related filter template; generating a plurality of candidate frames by taking the central point of the target position response diagram as the center, and sending the previous frame of video image, the target frame position information, the current frame of video image and the position information of the plurality of candidate frames into an improved dual modulation target estimation network to obtain the position information of the tracking target; according to the invention, the target mask with the target position information is calculated by using the gray frame difference method, the number of iterations of a correlation filtering algorithm is reduced through filtering of the target mask, and the tracking speed is increased.

The invention discloses a multi-target real-time tracking method based on key frame memory and target specificity filtering. The method comprises the following steps: acquiring a current to-be-monitored video frame for target detection to obtain a to-be-tracked target set; dividing a to-be-tracked target into a high-score detection target and a low-score detection target; and obtaining a tracked target before the current to-be-monitored video frame, matching the tracked target and the high-resolution detection target as well as the tracked target and the low-resolution detection target according to the similarity matrix, and updating the key frame memory and the corresponding track of the tracked target according to the matching result. According to the invention, the authenticity and accuracy of the tracking trajectory can be ensured, and the real-time tracking efficiency of the video can be effectively improved.