7 results about "Object dynamics" patented technology

The embodiment of the application provides a kind of method and system based on the object dynamic generation of reflection mechanism, it is related to the technical field of C++ programming technique.The method comprises the following steps: establishing the mapping relationship between multiple class identifiers and multiple creator functions;Receive configuration file, and determine at least one object definition item based on the configuration file;According to the class identifier contained in the target object definition item, retrieve the corresponding creator function from the mapping relationship;According to the construction parameter, call the creator function to obtain the object instance corresponding to the instance identifier contained in the target object definition item.Through the application, the problem of low efficiency of instance object development is solved, and the effect of improving the efficiency of instance object development is achieved.

The invention relates to the technical field of abnormal object recognition, in particular to an abnormal object dynamic behavior pattern recognition method based on a graph neural network, a medium and equipment, and the method comprises the steps: enabling the feature representation of each node to be capable of continuously fusing the weighted information of multi-order neighbors through a multi-layer iteration and edge weight-based message transmission mechanism, and enabling the feature representation of each node to be more accurate. Therefore, local and direct association is expanded into global and potential network mode representation, and behavior collaboration of direct / indirect association objects is captured. Similarity calculation and clustering analysis are performed on the Nth-layer intermediate feature vector generated in the graph neural network, and nodes with similar behavior patterns and similar structure roles in the network can be automatically collected into a cluster, so that individual anomaly can be judged, and the probability of individual anomaly can be reduced. And accurate locking and mode classification of the whole hidden collaborative abnormal gang which has no strong direct connection but highly collaborative behaviors can be realized, so that the accuracy and comprehensiveness of an identification result are greatly improved.

The invention provides an object dynamic change monitoring method and system based on an unmanned aerial vehicle image, and relates to the technical field of image analysis. The method comprises the steps of obtaining an image data set of a target area, performing target tracking processing to obtain a plurality of track fragments, determining a plurality of scene structure components, performing rhythm analysis on the target area, and constructing rhythm portraits of the plurality of scene structure components. The method comprises the following steps: acquiring real-time monitoring data of a target area, performing anomaly detection, generating structural behavior anomaly detection data of the target area, constructing an interaction graph and a dynamic flow constraint model of the target area, extracting behavior density data and behavior blocking data of the target area, identifying a plurality of local abnormal flow areas of the target area, and generating a dynamic flow constraint model of the target area; and performing diffusion influence identification on the local abnormal flow area to determine a plurality of flow abnormal areas, and fusing the structure behavior anomaly detection data to generate a dynamic change monitoring result of the target area. According to the method, the real-time sensing capability of progressive risk accumulation in the dynamic behavior of the object is improved.

The application relates to the technical field of abnormal object recognition, in particular to an abnormal object dynamic behavior pattern recognition method based on a graph neural network, a medium and equipment, through a multi-layer iteration and edge weight-based message passing mechanism, feature representation of each node can continuously fuse weighted information of multi-order neighbors, thereby expanding local and direct correlation into global and potential network mode representation, and behavior coordination of directly / indirectly correlated objects can be captured; through similarity calculation and clustering analysis on the Nth layer intermediate feature vector generated by the graph neural network, nodes with similar behavior patterns and similar structure roles in the network can be automatically collected into a clustering cluster, so that not only individual abnormalities can be determined, but also the whole implicit cooperative abnormal gang without strong direct connection but with high behavior coordination can be accurately locked and pattern classified, and the accuracy and comprehensiveness of the recognition result are greatly improved.

The invention discloses an intelligent parking lot safety monitoring method and system based on machine vision, and relates to the technical field of intelligent safety monitoring. The method comprises the steps that a parking lot image set on a time sequence is collected firstly, an effective image set capable of reflecting key changes is obtained through effective image extraction, redundant data are reduced, and the follow-up processing efficiency is improved; preprocessing the effective image set, and optimizing the image quality; identifying an object by using an enhanced YOLOv8 model to obtain an identified object information set; then determining a time sequence behavior sequence of each identified object to facilitate comprehensive tracking of object dynamics; abnormal judgment is carried out according to the behavior sequence, and potential safety risks can be found in time; and the abnormal object is labeled and compared with the database to obtain a monitoring result, so that efficient safety early warning can be realized. According to the scheme, data value mining and processing efficiency are considered through effective image set construction, and risk identification accuracy is enhanced according to the enhancement model and behavior analysis, so that safety monitoring efficiency is improved.

The application discloses a kind of multi-target operation object dynamic selection method and system, it is related to computer vision and intelligent video analysis technical field, specifically includes the following steps: S1, obtains the continuous video frame sequence that industrial assembly line monitoring equipment gathers;S2, object detection and tracking are carried out to the video frame, and the object set containing object detection frame, category label and track ID is output;S3, based on the spatial position relationship between object, contact state and function association, construct combination relation graph, identify single body object and the combination target formed by multiple objects.The multi-target operation object dynamic selection method and system, by object combination relationship modeling, multiple object combinations formed operation unit (such as "cover+box", "bolt+nut") can be identified, avoid to combine target error split into multiple independent objects;In automobile parts assembly scene test, the combination target identification accuracy reaches 94.2%.