395results about How to "Reduce redundant information" patented technology

The present invention relates to the field of security technology, and in particular to a fine granularity real-time supervision system based on edge computing. A fine granularity real-time supervision system based on edge computing is provided, comprising: an intelligent video monitoring device, an edge computing module, an edge computing node, and a cloud computing center. The system can reduce the redundant information of the system and realizes fine granularity management.

The invention discloses a video keyframe extraction method, which comprises the steps of: using a ViBe algorithm fused with an inter-frame difference method to perform moving object detection on an acquired original video sequence, so as to obtain a key video sequence containing a moving object; performing keyframe crude extraction on the key video sequence by using a global characteristic peak signal-to-noise ratio to obtain candidate keyframe sequences; and establishing global similarity of the candidate keyframe sequences by using the peak signal-to-noise ratio, establishing local similarity of the candidate keyframe sequences by using SURF feature points, and performing weighted fusion on the global similarity and the local similarity to obtain comprehensive similarity, performing self-adoptive keyframe extraction on the candidate keyframe sequences by using the comprehensive similarity, and finally acquiring a target keyframe sequence. The video keyframe extraction method providedby the invention can effectively extract the video keyframes, obviously reduce the redundant information of video data, and express the main content of the video concisely. Moreover, the video keyframe extraction method has low algorithm complexity and is suitable for real-time extraction of keyframes of surveillance videos.

Disclosed is a multi-angle adaptive intra-frame prediction-based point cloud attribute compression method. A novel block structure-based intra-frame prediction scheme is provided for point cloud attribute information, where six prediction modes are provided to reduce information redundancy among different coding blocks and improve the point cloud attribute compression performance. The method comprises: (1) inputting a point cloud; (2) performing point cloud attribute color space conversion; (3) dividing a point cloud by using a K-dimensional (KD) tree to obtain coding blocks; (4) performing block structure-based multi-angle adaptive intra-frame prediction; (5) performing intra-frame prediction mode decision; and (6) performing conversion, uniform quantization, and entropy encoding.

The invention relates to a method, device, a readable storage medium and a computer device for text translation, including steps: obtaining an initial source text and reconstructing that source text,wherein the reconstructed source text is the source text obtained by supplementing the initial source text with missing word position information; carrying out semantic coding on the initial source text to obtain a source end vector sequence corresponding to the initial source text; a target end vector being obtained by sequentially decoding that source end vector sequence, and the target end vector being decoded according to the word vector of the candidate target word determined before each decoding, and the candidate target word of the current time being determined according to the target end vector of the current time; forming a target end vector sequence by sequentially decoding the target end vectors; performing reconstruction evaluation processing on the source vector sequence and the target vector sequence according to the reconstruction source text to obtain a reconstruction score corresponding to each candidate target word; a target text being generated according to that reconstruction score and the candidate target word. The scheme provided by the present application can improve the quality of translation.

The invention belongs to the technical field of radar target identification and provides a one-dimension range profile optimal orthogonal nolinear subspace identification method for radar targets. Nonlinear transformation is conducted on a one-dimension range profile of each category of targets, the one-dimension range profile is mapped to high-dimensional linear characteristic space, an optimal orthogonal nolinear transformational matrix is established in the high-dimensional linear characteristic space, characteristic extraction is conducted, a nearest neighbor rule is adopted for classification, and the category of an input target is finally determined. The method comprises the steps of utilizing a kernel function and the one-dimension range profile of the radar target to train a vector to determine matrixes of Ui, Vrj, (K)ij, W alpha and B alpha; determining a vector alpha i (i=1, 2, ..., n) in optimal orthogonal nolinear subspace, determining the transformational matrix A of the optimal orthogonal nolinear subspace, wherein the A ranges from alpha 1 to alpha n; determining a base template vector of the target; determining an optimal orthogonal nolinear projection vector of the one-dimension range profile xt of the input target; determining the Euclidean distance between the optimal orthogonal nolinear projection vector and the base template vector of the target and determining the category of the one-dimension range profile of the input target. The one-dimension range profile optimal orthogonal nolinear subspace identification method for radar targets can effectively improve target identification performance.

The present invention discloses an optical flow computation method using a time domain visual sensor, and specific implementation steps are given out. Different from a current method for performing optical flow computation by adopting "a frame sequence image (video)", the method disclosed by the present invention uses a visual information acquisition device-time domain visual sensor to perform optical flow computation of a field of view. Furthermore, because each pixel autonomously performs change detection and asynchronous output, motion and change of an object in a scenario can be sensed in real time, a continuity assumption of "no change in brightness and no change in local speed" in a differential optical flow computation method is well satisfied, and the real-time property of optical flow computation is greatly improved while the precision of optical flow computation is significantly improved. Therefore, the method is very suitable for optical flow computation and subsequent tracking and speed measurement of the high-speed moving object.

The invention relates to a video compression method based on sparse samples, and belongs to the technical field of video compression. The method comprises the steps: S1, data preprocessing; S2, firstly, learning each frame of a video in a data set by utilizing a variation auto-encoder through a video generation method combining the variation auto-encoder and a generative adversarial network, constructing a hidden space with good continuity, and enabling each point in the hidden space to correspond to one frame in the video; inputting the noise and the text into a generator of the generative adversarial network, enabling the generator to generate a plurality of associated points in the latent variable space, and finally generating continuous images are generated through a decoder of a variational auto-encoder; S3, inputting the generated continuous images into a video compression model, screening background frames through a CNN network, and then identifying a target in each frame of image by using a YOLO neural network. According to the invention, the video compression efficiency can be improved, and the network transmission delay and the consumption of local resources are reduced.

System and methods are provided configured to reveal differences in content, such as commercial and program content. An input port may be configured to receive a first plurality of bit stream audio channels. A circuit may optionally be configured to enhance artifacting, noise, and / or bit rate errors and may optionally reduce redundant signal information. A signal processor circuit may be configured to transform the first plurality of bit stream audio channels into a frequency domain. A spectrum analysis system may be configured to compare properties of the first plurality of bit stream audio channels to one or more reference signals. An identification circuit may be configured to identify different content types included in the first plurality of bit stream audio channels.

Disclosed is a multi-angle adaptive intra-frame prediction-based point cloud attribute compression method. A novel block structure-based intra-frame prediction scheme is provided for point cloud attribute information, where six prediction modes are provided to reduce information redundancy among different coding blocks and improve the point cloud attribute compression performance. The method comprises: (1) inputting a point cloud; (2) performing point cloud attribute color space conversion; (3) dividing a point cloud by using a K-dimensional (KD) tree to obtain coding blocks; (4) performing block structure-based multi-angle adaptive intra-frame prediction; (5) performing intra-frame prediction mode decision; and (6) performing conversion, uniform quantization, and entropy encoding.

The invention relates to a breakpoint simulation controller and a breakpoint simulation control method for a ground simulation system. The controller comprises a breakpoint monitoring storage module, a data recovery module and a time synchronizing module; under the semi-physical simulation condition, the breakpoint monitoring storage module records the global variable of the simulation system; when the simulation restarts, the data recovery module takes the data as input, so that continuation of system simulation is realized, the simulation efficiency and the simulation effect are greatly improved, and massive time, energy and cost are saved; meanwhile, the controller comprises the time synchronizing module, so that synchronization of accuracy record and recovery time of the system operating breakpoint is ensured; and by using the controller and the method, judgment of multi-process breakpoints of the complex simulation system is solved, the process data are stored in time, and data and means are provided for recovering the operation of the system.