7results about How to "Real-time estimate" patented technology

The invention provides a vehicle-mounted camera dynamic external parameter estimation method, system and device and a medium, and the method comprises the steps: extracting angular points of a previous-frame downsampling image to obtain an initial angular point set, and filtering the initial angular point set according to a previous-frame target detection result to obtain a previous-frame angular point set; performing angular point tracking on the angular point set of the previous frame by adopting an angular point tracking algorithm through various of the vanishing point of the previous frame, the vanishing point of the lane line of the previous frame, the vanishing point of the lane line of the current frame, the target detection result of the previous frame and the target detection result of the current frame to obtain an angular point set of the current frame; and acquiring a four-degree-of-freedom affine transformation matrix of the current frame, a vanishing point of the current frame and a yaw angle rate through the angular point set of the previous frame and the angular point set of the current frame. According to the invention, external parameter estimation is carried out by combining the vanishing point of the lane line and the target detection result, so that the stability of pixel tracking is enhanced, the efficiency of a tracking algorithm is improved, and real-time, continuous and accurate external parameters of the camera are realized.

The present invention relates to the technical field of vehicle-mounted self-organizing networks, and provides a vehicle density estimation method based on perception message broadcasting and probability derivation. The density estimation is performed using the perception messages periodically broadcast by vehicles in a vehicle-mounted self-organizing network. First, each neighbor node is analyzed. i The average position of the nodes and the number of messages received are used to calculate the point-to-point packet reception probability. The PRP function that varies with the receiving distance is then obtained. The node perception rate and the average perception ratio (AAR) are calculated through probability deduction. Finally, the vehicle density is estimated by combining the number of perceived vehicles and the AAR. The whole process does not require additional communication overhead and only relies on the received perception messages. The impact of message loss on density estimation is considered through a probabilistic model, which is used to accurately evaluate vehicle density and facilitate congestion control, so as to improve the reliability and accuracy of vehicle communications and the safety of real-time traffic.

The invention relates to a polycrystalline silicon reduction furnace soft measurement model construction method, a polycrystalline silicon reduction furnace control method and computer equipment. The method comprises the following steps: determining an input variable set and an output variable set; the output variable set comprises in-furnace definition, and the input variable set comprises hydrogen flow, silicon rod diameter, heating power and cooling water flow; determining a lag time constant set; constructing an in-furnace atmosphere temperature estimation sub-model; constructing a to-be-trained model structure based on the input variable set, the output variable set, the lag time constant set and the in-furnace atmosphere temperature estimation sub-model; training the to-be-trained model structure based on historical operation data of the polycrystalline silicon reduction furnace to obtain a corresponding soft measurement model; the soft measurement model is configured to receive real-time data of the input variable set and output at least an in-furnace sharpness estimate. By adopting the method, the real-time and accurate estimation of the definition in the furnace can be realized, and an accurate state feedback basis is provided for the multivariable cooperative control of the polycrystalline silicon reduction furnace.

The invention discloses a low-voltage transformer area power grid three-phase dynamic state estimation method based on a data fusion technology. The method comprises the following steps: a, establishing a low-voltage transformer area power grid measurement equation; b, unscented transformation and a dynamic prediction method; the low-voltage transformer area power grid three-phase DSE method based on the DF technology has the advantages that the operation state of the low-voltage transformer area power grid is accurately estimated in real time by combining the mixed measurement data of the intelligent electric meter and the synchronous phasor measurement unit (PMU) and utilizing the data fusion technology, powerful support is provided for safe and economical operation of the power grid, and the reliability of the low-voltage transformer area power grid is improved. The method also has relatively high adaptability and expandability, and can meet the state estimation requirements of different low-voltage transformer area power grids.

The application discloses a robust micro-expression segment segmentation method and system which avoids head movement, adopts computer vision tools to recognize head posture of each frame image in a video sample, and extracts translation variable and rotation variable of the head posture relative to the same coordinate axis; adopts frame difference method to calculate head movement amplitude, divides the video sample into several continuous frame intervals with head movement amplitude less than a set displacement threshold; adjusts and aligns each frame of the video sample with continuous frame intervals to obtain a micro-expression sample; the system comprises a video sample library, a vision tool, a calculation module, a comparison module, a video segmentation module, a micro-expression processing module and a micro-expression sample library; the application divides small-amplitude head movement video segments which can be used as micro-expression samples, avoids the influence of head posture on micro-expression detection, eliminates the influence of head movement within a certain displacement threshold by alignment or removal of global motion, and improves the performance of micro-expression detection.

The present invention discloses a monocular 3D gesture estimation method based on a fully convolutional neural network, comprising the following steps: obtaining a hand image and preprocessing the image; constructing a fully convolutional 3D gesture estimation network and training the fully convolutional 3D gesture estimation network; inputting the preprocessed image into the fully convolutional 3D gesture estimation network to predict the final two-dimensional coordinates of key points and the relative depth of each key point; post-processing the predicted two-dimensional coordinates of the key points and the relative depth to calculate the three-dimensional coordinates of the hand key points. The present invention can effectively deal with the scale uncertainty problem in monocular 3D gesture estimation by decoupling the hand scale information from the depth prediction process of the hand key points by the neural network. In practical applications, by obtaining accurate prior scale information, the present invention accurately restores the actual depth of the hand key points in the scene relative to the imaging device, effectively improving the upper limit of the accuracy of the 3D gesture estimation method and the generalization ability of the scene.

The invention discloses a real-time fruit counting method based on visual SLAM and target detection, which uses a Realsense D435i depth camera combined with a SLAM algorithm to obtain the position information and image information of the camera in an orchard at a certain moment; the YOLOV5 target detection algorithm is used to detect the fruit in the image through the acquired color image and depth image, and the fruit pixels are mapped to a three-dimensional space, and the RANSAC algorithm is used to fit the center SC and radius r of each fruit sphere; the fruit sphere is converted to the orchard world coordinate system in combination with the position obtained by the SLAM algorithm, and the three-dimensional position information (x, y, z) and radius information r of the identified fruit center are recorded; and a fruit de-duplication detection algorithm is used to accurately count the number of fruits. The method of the invention has the advantages of low cost, strong robustness and high real-time performance, and can estimate the number of fruits in real time in an orchard environment.