691 results about "Posture recognition" patented technology

The invention provides a human body posture detection method and equipment based on machine vision, which are applied to posture recognition in physical fitness detection, and are used for generating a dynamic human body region set of a target user by acquiring a multi-frame continuous image sequence of a physical fitness training scene and performing human body region positioning processing on the multi-frame continuous image sequence. Performing posture feature extraction processing on the dynamic human body region set to obtain a space-time posture feature set of the target user, performing posture state recognition processing on the space-time posture feature set based on a preset physical fitness evaluation model to generate a posture state recognition result of the target user, and generating physical fitness evaluation parameters according to the posture state recognition result, and the physical fitness evaluation parameters are fed back to the physical fitness training guidance system. According to the method, the identification capability of fine posture change in the fitness action of the complex body can be improved, the synchronous evaluation of the action specification degree and the physiological load state is realized, and the limitation of the traditional single-dimensional action evaluation is broken through.

The invention relates to a human shape posture recognition method and system based on image analysis, and the method comprises the steps: inputting the reference human shape data of a monitored object, and collecting the multi-modal data of an RGB image, an infrared image and inertial measurement data in a target scene in real time; space-time alignment processing is carried out, and corresponding features are fused; image space features are extracted, time sequence modeling is carried out on inertial data, and dynamic weighted fusion of two paths of network outputs is realized through a gating mechanism; human body basic joint points are positioned, and refined posture vectors including joint angles and limb relative positions are generated; according to attitude data and environment information collected in real time, adaptively adjusting an attitude classification threshold value and a similarity measurement standard, and predicting abnormal behaviors in a future time period; and when the abnormal behavior is predicted, triggering to execute a preset safety measure. Multi-modal data can be efficiently fused, attitude features can be accurately extracted, an identification strategy can be adaptively adjusted, and human shape attitude identification with behavior trend prediction capability can be realized.

The invention provides a posture recognition algorithm for any object under a monocular camera and an application system, and the algorithm comprises the steps: S1, constructing a target three-dimensional model, carrying out the multi-view annular shooting image collection of a target, and generating a dense grid model through feature extraction, matching, posture calculation and a multi-view geometric method; s2, generating an image depth map, and predicting depth information of a target in a motion process based on a monocular image sequence; s3, extracting a target image mask, and generating a target area mask graph through an image encoder, a prompt encoder and a mask decoder; and S4, executing attitude estimation, performing attitude initialization, correction and screening by combining the three-dimensional model, the depth map and the mask map, and outputting a six-degree-of-freedom attitude result of the target. According to the method, the target is subjected to annular shooting modeling through the method based on multi-view geometry, the three-dimensional model of the target is generated, attitude estimation is achieved in combination with the image mask and the depth map, the generalization ability of an attitude estimation algorithm in an actual scene is improved, and the application range of the attitude estimation algorithm in the actual scene is widened.

The invention relates to the technical field of automatic debugging, in particular to a digital twinning-based chip fixture debugging system, which comprises a state sensing fusion module, a positioning correction judgment module, a contact adjustment execution module, a closed-loop feedback correction module and a data linkage optimization module. According to the method, through time axis synchronous matching of multiple types of sensing data, extraction of position offset and pressure sudden change intersection features, enhancement of direction identification and dynamic precision, angle direction trend quantification through included angle interval analysis, improvement of spatial positioning continuity, and guarantee of effective response of an adjustment window through driving rate difference comparison, the accuracy of the adjustment window is improved, and the accuracy of the adjustment window is improved. Stretching and deformation data backtracking are introduced to improve the path correction capability, a virtual and real link is constructed in combination with a node log and a delay identifier, the consistency of simulation and actual behaviors is constrained, the overall debugging process is optimized through data linkage and time sequence control collaboratively, and higher response timeliness, posture recognition precision and data fusion stability are achieved.

The invention relates to the technical field of rehabilitation training detection, in particular to a rehabilitation training detection method and system based on artificial intelligence, a standard action library is constructed through standard action videos shot at multiple angles, and track and angle features of key joints are extracted for user training comparison; the skeleton key points of the user are extracted in real time through a MoveNet network, and efficient posture recognition in a home scene is achieved; analyzing position difference, angle change and acceleration characteristics by combining a space-time sequence matching algorithm, generating a dynamic matching degree index, and positioning a deviation joint to generate a correction prompt; introducing an attention mechanism model, learning the contribution degree of each joint to cycle recognition, dynamically selecting a dominant joint for action counting, recognizing starting and ending points of an action cycle through an acceleration curve, and finishing effective action statistics in combination with a dynamic threshold value, so that the counting accuracy and the self-adaptive capability are improved; therefore, the training cost is reduced, the evaluation credibility is enhanced, and accurate statistics and analysis of rehabilitation training data are realized.

The invention discloses a human motion posture recognition method and system based on multi-modal data fusion, and relates to the technical field of posture recognition, and the method comprises the steps: firstly, synchronously obtaining a human motion posture video frame and an IMU segment, then carrying out the feature extraction of the two kinds of heterogeneous data in a shunt parallel mode, and carrying out the feature extraction of the two kinds of heterogeneous data; and respectively capturing visual space attitude information and dynamic inertial characteristics of the IMU. Afterwards, specific features of the modals are uniformly expressed through a mixed Token and embedding mechanism, and a space-inertia collaborative attention fusion mechanism is further introduced to realize dynamic association and deep fusion of cross-modal information; and finally, classifying the multi-modal fusion representation vector obtained by fusion so as to realize accurate recognition of the human motion posture. In this way, the defect that traditional fusion is insufficient in capturing subtle action differences can be overcome, and the accuracy and stability of action recognition in a complex scene are improved.

The invention relates to the technical field of multi-modal posture recognition, and relates to a multi-modal posture recognition system based on Transform fusion, which fully excavates the complementary advantages of visual information in spatial detail representation and inertial information in time sequence dynamic capture through space-time alignment of multi-modal data and deep fusion based on an attention mechanism. The accuracy and the stability of attitude estimation under the conditions of visual occlusion, rapid movement and complex illumination are obviously improved; posture optimization is carried out by introducing physical constraints such as bone length constancy and joint movement range limitation, and time domain smoothing and contact state correction are applied, so that the precision of the generated three-dimensional posture sequence is ensured, a solid technical foundation is laid for improving the naturalness, safety and intelligent level of man-machine interaction, and the method is suitable for popularization and application. And meanwhile, reliable application and deep development of the intelligent robot in key fields of service robots, virtual reality, remote cooperation and the like are powerfully promoted.

The invention provides a physical training posture correction method based on machine vision, which comprises the following steps: acquiring an athlete training image sequence through a multi-view image acquisition system and preprocessing the athlete training image sequence, extracting three-dimensional posture key points of a human body by utilizing a depth posture recognition model, constructing a skeleton model and calculating real-time kinematics characteristic parameters, and performing multi-dimensional comparison with standard parameters to generate a posture deviation evaluation report, thereby generating a personalized multi-modal correction feedback scheme, and establishing a personal athlete movement feature database to realize adaptive standard parameter optimization. According to the method, the training postures of athletes can be accurately recognized and corrected in real time, the training effect can be improved, sports injuries can be prevented, and training individuation and scientificity are improved.

The invention is suitable for the technical field of artificial intelligence, and provides a TOF camera hardware-based personnel falling intelligent monitoring method and system, and the method comprises the following steps: collecting three-dimensional point cloud data of a monitoring region through a ToF area-array camera, carrying out the data preprocessing and background subtraction of the three-dimensional point cloud data, and obtaining the point cloud data of a human body; personnel existence detection and personnel static detection are carried out based on the three-dimensional point cloud data; performing posture recognition, performing normalization conversion on the human body point cloud data to obtain a human body depth map, and performing posture detection on key points of the human body depth map to obtain human body key point data; and predicting the human body key point data based on the trained LSTM model to obtain a person posture state, and when the person posture state is that the person falls down, generating early warning information. The method is wide in application range and suitable for personnel falling detection in public areas and private spaces, the point cloud data only comprises the attitude key points of the personnel, and personnel falling reasons can be known under the condition that privacy is not invaded.

The invention discloses a three-dimensional human body posture estimation method and system, and relates to the technical field of computer vision, and the method comprises the steps: extracting two-dimensional human body posture key points from a monocular video picture sequence, and generating a two-dimensional human body posture key point sequence; projecting the two-dimensional key point sequence to a feature space through nonlinear high-dimensional mapping to generate a high-dimensional feature space matrix; and inputting the high-dimensional feature matrix into a three-dimensional human body posture recognition model fusing motion constraints and frequency division spatial-temporal features to obtain a three-dimensional human body posture key point sequence, and realizing three-dimensional human body posture estimation through three-dimensional coordinates. According to the method, the robustness and the detection precision of the monocular three-dimensional human body posture estimation method are improved. Error values of relative movement speed, skeleton length and skeleton direction of the key points are calculated, so that training is easier to converge, and the training process is more stable.

The invention provides a driving behavior intelligent identification method and system based on video analysis, and the method comprises the steps: obtaining a driver face video stream and a road environment video stream collected by a vehicle-mounted camera, and reading the driving information recorded by a whole vehicle communication network; recognizing an eyelid closing state, a sight line direction and a head posture in the driver face video stream based on a posture recognition model, and performing fatigue distraction analysis to obtain driver state information; performing motion trail analysis on the road environment video stream and the driving information, and performing driving risk assessment in combination with the driver state information to obtain driving assessment information; and performing early warning construction according to the driving evaluation information, generating early warning prompt information, and synchronously writing the early warning prompt information, the driving evaluation information and the driver state information into a safety data protection memory. The fatigue and distraction states of the driver can be recognized more accurately, and the accuracy of state judgment is improved.

The present invention provides a solution of a human-computer interaction method and system based on three-dimensional coordinate measurement, and a plurality of solutions for applying same to solve technical problems. An operation body and / or an operator performs input and operation interaction by means of a virtual keyboard and a virtual mouse, or integrates a display picture space and carries out various participatory and interactive direct operations and experiences on object components and objects in virtual and real scenes. Virtual handwriting input, body movement posture recognition, and lip recognition interaction methods are further provided, and virtual mode control and interaction methods such as gesture recognition and virtual touch screen interaction are supported. Contact is not needed and there is no need to touch the screen, and mobility is achieved. The present invention is suitable for the two-dimensional conventional computing field, and particularly suitable for three-dimensional space computing fields (comprising VR, MR, AR, meta-universe, virtual simulation, software games, three-dimensional graphics, three-dimensional animation, and other three-dimensional scenes). Human-computer interaction operations in work and life of people can be greatly facilitated and optimized.

The invention discloses a millimeter-wave radar sparse point cloud-based human body posture recognition method, which belongs to the technical field of human body posture recognition, and comprises the following steps of: acquiring three-dimensional point cloud data of human body actions through a millimeter-wave radar, and preprocessing the three-dimensional point cloud data; and training a lightweight neural network model by using the preprocessed point cloud data, wherein the model comprises an edge convolution module and a grouping sparse Transform encoder module. The edge convolution module extracts spatial geometric features and detects dynamics, static postures are directly classified, dynamic postures capture a time sequence dependency relationship through a grouping sparse Transform module, attention calculation is only carried out on frames with feature changes exceeding a threshold value, and mean pooling aggregation is carried out on other frames. And finally, classifying the human body postures based on the spatial geometric features and the time sequence dependency relationship to obtain a classification result. The device is simple in structure, accurate in recognition and suitable for efficient deployment of edge equipment.

The invention provides an intelligent warehouse goods posture recognition and automatic sorting method and system, and the method comprises the steps: S2, extracting the edge contour and surface features of goods from point cloud data for a preliminary value set, calculating the direction vector and shape distribution characteristics of the goods through a principal component analysis method, and obtaining a quantitative result of shape feature extraction; s6, historical sorting data and real-time sensor data are extracted from the warehousing system database according to the classification basis adapted to the complex scene, the dynamic posture change trend of the goods is predicted through a time sequence analysis method, and optimization parameters of real-time processing are obtained; and S7, the movement track and the grabbing angle of the sorting mechanical arm are adjusted through the optimization parameters subjected to real-time processing, a deep reinforcement learning algorithm is adopted to conduct iterative optimization on the sorting action sequence, and an execution scheme of sorting accuracy is determined. According to the method, the accuracy of goods posture recognition and the automatic sorting efficiency in the complex storage environment are remarkably improved.

The invention relates to the technical field of computer vision and neural network processing, in particular to a human body posture recognition method based on a multi-modal neural network framework. Comprising the following steps: collecting event data, and representing the event data by using a five-channel event representation method; dividing the preprocessed five-channel event data into three channels, namely a space channel, an event characteristic channel and a point cloud channel; after channel splitting is completed, feature extraction is carried out respectively, and features of different modals are optimized and integrated through dynamic feature enhancement and cross-modal fusion; a skeleton constraint module and a space-time joint processing module are adopted to enhance understanding of the network on a human body structure and modeling of a space-time dependency relationship; a prediction head module is adopted to output final human body key point coordinates; and carrying out training optimization by adopting a multi-task learning framework to obtain a posture recognition result. The method has the advantages that the sparse asynchronous characteristic of event data is effectively processed, the blind spot problem of static limbs is solved, low power consumption is kept, and high-precision human body posture estimation is achieved.

The invention relates to the field of livestock breeding, in particular to a cattle body size performance measurement system based on a binocular camera, which comprises a binocular camera acquisition module, a posture recognition and key point extraction module, a point cloud processing module, a posture correction module and a data processing and optimization module. According to the invention, the binocular camera is combined with an advanced algorithm, so that the measurement efficiency in the cattle breeding process is greatly improved; through cooperative work of multiple modules, reliable data support is provided for cattle breeding; manual measurement is not needed in the whole process, manual operation errors are avoided, the labor intensity of workers is reduced, meanwhile, stress reactions of cattle caused by manual fixing and measurement are reduced, and healthy growth of the cattle and scientization and humanization of breeding management are facilitated; the system can adapt to different light conditions, cattle body types and motion states, can stably and accurately complete body size performance measurement tasks no matter in an indoor breeding environment or in an outdoor pasture environment, and has a wide application prospect.

The invention relates to the technical field of intelligent training, in particular to an intelligent shooting examination training system and method.The intelligent shooting examination training system comprises a posture recognition module, a track monitoring module, a hit analysis module, a rhythm comparison module and a label output module. According to the method, on the basis of action images in the process from gun holding to firing of a trainee, synchronous angle tracks of shoulders and elbows are recognized in real time, comparison is conducted in combination with a standard action combination, the matching difference in each time period is accurately screened and quantified, and the action control stability is dynamically evaluated by analyzing laser track coordinate points and direction changes in the aiming period; structured scores are formed by using a hit point sequence and combining path ductility and hit area density, action unit starting and ending time in a training process is compared with rhythm arrangement one by one, advanced and delayed phenomena are identified, performance scores are flexibly adjusted according to multi-dimensional index weights, and accurate matching between training performance evaluation and actual ability requirements is realized.

The invention discloses a shoe tree grabbing robot track planning system and method based on multi-modal sensing. The system comprises a shoe tree storage area, a mechanical arm grabbing assembly, a multi-modal sensing device, a posture recognition module, an interference sensing module, a grabbing point optimization module, a stable window generation module, a path planning module, a fine adjustment control module and a self-learning module. The system extracts the posture of the shoe tree by fusing visual images, structural sound waves and infrared contour information, then utilizes interference fringes to analyze and judge the material and friction characteristics, combines stability to judge and screen a grabbable target, and executes posture fine adjustment and fitting at the tail end of a clamping jaw through a touch sensing film. And the grabbing failure information is used for updating the strategy database to realize self-adaptive optimization of the grabbing strategy. The method is suitable for grabbing special-shaped objects and is high in robustness and success rate.

The invention discloses a manipulator grabbing control method and equipment based on position and posture recognition. The manipulator grabbing control method comprises the following steps that S1, initial image information of a target object is acquired through image acquisition equipment; s2, performing preprocessing and feature extraction on the initial image information, and obtaining three-dimensional position coordinates and attitude parameters of the target object through a preset position and attitude recognition algorithm; and S3, according to the three-dimensional position coordinates and the posture parameters, a grabbing path of the manipulator is planned by combining a kinematic model of the manipulator. By introducing technical means such as a deep learning recognition algorithm, forward / inverse kinematics model collaborative planning, real-time posture dynamic adjustment and force sensing feedback control, the problems that a traditional mechanical arm is low in grabbing precision, poor in adaptability and insufficient in operation stability are solved, intelligent and high-precision grabbing control in a complex scene is achieved, and the grabbing precision of the mechanical arm is improved. And the requirements of the modern industry on high efficiency, reliability and flexibility of automatic equipment are met.

The invention discloses a thermal comfort monitoring robot system and method based on multi-modal body language feature recognition and fusion, and aims to realize individual thermal comfort monitoring, regulation and control in a dynamic indoor environment. The intelligent mobile platform carries multiple types of environment sensors and visual acquisition equipment, and performs autonomous navigation and acquires environment parameters and human body image data; the multi-modal feature recognition module performs human body detection, posture recognition and identity maintenance on a video stream frame by frame based on a deep learning network, and extracts multi-modal human body features; the data processing and fusion module realizes cross-frame identity consistency through an algorithm and fuses multi-modal human body features and environmental parameters; the thermal comfort calculation module dynamically adjusts a metabolic rate parameter based on a predicted average voting model, incorporates the metabolic rate parameter into a psychological correction factor and calculates an individual thermal comfort index; the three-dimensional visualization and regulation and control module generates a comfort distribution map, is in butt joint with a building environment automatic control system to achieve dynamic regulation and control, and provides support for intelligent building environment management.

A hand pose recognition method is performed by a computer device, including: acquiring a current frame of a multi-lens video of a target object; performing hand detection on a first view of the current frame to obtain a first lens detection result; performing hand estimation on a second view of the current frame to obtain a second lens estimation result; removing, from the hand detection boxes in the first view and the hand estimation boxes in the second view, redundant boxes corresponding to redundant hands, and then performing hand joint point recognition on remaining boxes to obtain two-dimensional joint points; converting the two-dimensional joint points into three-dimensional joint points in a three-dimensional hand coordinate system; and converting the three-dimensional joint points in the three-dimensional hand coordinate system into three-dimensional joint points of the current frame in a world coordinate system according to pose estimation parameters corresponding to the current frame.

The invention belongs to the field of power distribution network monitoring, and provides a power distribution box damage behavior monitoring method and system based on human body posture estimation, and the method comprises the steps: obtaining a monitoring video of a power distribution box terminal for preprocessing, and obtaining a preprocessed video frame; based on the preprocessed video frame, performing posture recognition by using a pre-trained human body and distribution box posture recognition model to obtain a human body and distribution box posture recognition result; key point tracking and identity association are performed by using a human body and distribution box posture recognition result, a space-time skeleton diagram is constructed, and the space-time skeleton diagram is screened to generate a skeleton space-time sequence; on the basis of the skeleton space-time sequence, a pre-trained space-time diagram convolutional network model is utilized to identify a damage behavior, and a damage behavior identification result is obtained; when the confidence coefficient in the damage behavior recognition result exceeds an alarm threshold value, alarm information is generated, and an alarm process and safety linkage control are triggered. According to the invention, accurate monitoring and real-time early warning of the damage behavior of the distribution box are realized.

The invention discloses a mechanical arm control method and device based on human body posture recognition and a storage medium, and relates to the technical field of robots. The mechanical arm control method comprises the steps that human body skeleton joint point coordinate information, collected by a sensor arranged on a robot, of an operator is obtained; on the basis of a preset action recognition model, action type recognition is conducted on the human skeleton joint point coordinate information, and the action type of the operator is obtained; determining the intention of the operator according to the scene information, the action type and the movement track of the operator; and determining a target task associated with the intention of the operator, and controlling the robot to execute the target task. In the invention, the robot can dynamically adjust the task execution strategy according to the real-time action and intention of the operator, so that the task execution efficiency and flexibility are improved.

The invention relates to an application awakening method based on an NFC tag and an NFC acceptance terminal. The NFC acceptance terminal comprises an NFC label module used for providing an NFC label supporting a read-write form; the camera is used for acquiring a posture picture of a mobile terminal close to the NFC acceptance terminal; the posture recognition module is used for recognizing the approaching posture of the mobile terminal approaching the NFC acceptance terminal according to the posture picture, searching a corresponding relation table according to the approaching posture to obtain application configuration information of an application corresponding to the approaching posture, and writing the application configuration information into the NFC tag, the corresponding relation table stores a one-to-one corresponding relation between a plurality of approaching postures of the mobile terminal approaching the NFC acceptance terminal and application configuration information of a plurality of applications. According to the invention, a plurality of different applications can be supported in one NFC acceptance terminal without modifying the mobile terminal.

The invention discloses a natural growth apple three-dimensional attitude recognition method based on a real-time multi-task fusion algorithm, and the method comprises the steps: obtaining an aligned RGB-D image through a depth camera, constructing a data set in combination with an image enhancement technology, and synchronously training a classification, segmentation and key point detection model through a multi-task deep learning framework; judging the contour form of the apple through the classification model, and carrying out contour fitting by adopting a convex hull algorithm and an arc adjacency matrix ellipse detection algorithm; two-dimensional centroid positioning and three-dimensional point cloud analysis technologies are fused, and bimodal calculation in the three-dimensional fruit axis direction is realized in combination with a fruit stem and calyx key point detection result. According to the method, the precision problem of apple posture recognition in a complex natural environment is effectively solved, the anti-interference capability is remarkably improved through the three-dimensional point cloud denoising and feature value analysis technology, and reliable technical support is provided for automatic picking and growth monitoring.

The invention relates to the technical field of medical instrument data monitoring, and provides a limb artificial joint motion signal posture and wear monitoring system which comprises a motion posture recognition module, a motion signal processing module and a motion signal processing module. On the basis of the collected motion signal data, the motion posture is judged and analyzed according to a motion posture judgment model, and the type of the motion posture is recognized; wherein the motion posture types comprise a high-load motion posture, a low-load motion posture and a static posture; and the monitoring frequency control module is used for extracting motion characteristics. The monitoring frequency control module dynamically adjusts the wear monitoring frequency according to the motion posture and the motion intensity, introduces a joint fatigue factor to comprehensively measure the motion intensity, improves the monitoring frequency during high-load motion, timely captures rapid wear changes, reduces or stops monitoring during low-load motion or static, reduces resource waste, and improves the working efficiency. Accurate and efficient monitoring is realized, and the monitoring accuracy and timeliness are improved.

The invention belongs to the field of tire skin space pose detection, and particularly discloses a space pose recognition method and system for disorderly stacked tire skins. Comprising the following steps: obtaining an ROI of a target workpiece in a depth map through a convolutional neural network; converting the depth map into original point cloud data of the target workpiece according to the internal reference matrix of the depth camera and the ROI of the target workpiece in the depth map; preprocessing the original point cloud data, calculating a boundary point cloud of the original point cloud data, and fitting a three-dimensional circle by adopting an RANSAC algorithm based on the boundary point cloud to obtain a space pose of the tire skin; and calculating the proportion of the number of the point clouds in the three-dimensional circle in the boundary point clouds, and judging whether the spatial pose is accurate or not according to the proportion. According to the method, the limitation in traditional image detection and point cloud processing is effectively solved, higher adaptability and robustness are achieved, more accurate data support can be provided in a complex production environment, and the automation level and production efficiency of tire assembly are remarkably improved.

The invention discloses a posture recognition method and device based on a neural network and a medium, and relates to the technical field of posture recognition, and the method comprises the following implementation steps: configuring the IP address information of a posture recognition remote control area server; entering a posture capturing end, capturing a plurality of parameters of a human body three-dimensional posture in real time through a camera, generating an image, and judging whether image illumination is abnormal or not; entering a covering processing end, and determining the real shape of the posture according to the really required recognition posture; and entering a multi-mode combination end, combining posture recognition with sound and shape information of a human body, and realizing real-time interaction and data association of posture recognition. According to the posture recognition method and device based on the neural network and the medium, the situation that the accuracy of human body key point detection is affected by the image according to illumination change during posture recognition is avoided, the real shape of the posture to be recognized is timely and accurately obtained through a four-side detection means, and the accuracy and robustness of posture recognition can be guaranteed by combining sound and the shape.

The invention relates to the technical field of visual recognition, and particularly provides a bottle sorting-oriented three-dimensional visual posture recognition method and related equipment, and the method comprises the steps: obtaining three-dimensional point cloud data and RGB image data of bottles on a conveyor belt, carrying out the preprocessing, feature extraction and geometric analysis of the three-dimensional point cloud data, and obtaining six-degree-of-freedom posture parameters; and the six-degree-of-freedom attitude parameters are projected to RGB image data, the overlap ratio of the projection contour and the actual bottle contour is calculated, and when the overlap ratio is larger than a preset threshold value, it is confirmed that the attitude is effective, and the corresponding attitude parameters are output to a mechanical arm sorting control system. Six-degree-of-freedom posture parameters of the bottle body can be obtained through posture alignment and template point cloud registration, reliability confirmation of a posture recognition result is achieved through overlap ratio verification in an RGB image data domain, and the defect that the posture recognition result is not reliable under the conditions that the posture change of the bottle body is large, the appearance of the bottle body is diversified and the bottle body is inclined or shielded and interfered is overcome. The problems of insufficient recognition precision and incomplete attitude parameters exist.

The invention discloses a target identification processing method and system based on a video image. The method comprises the following steps: acquiring a series of frames from a video according to a time sequence, and taking the series of frames as a sequence image; selecting a predetermined image from the sequence images, and selecting a target to be recognized from the predetermined image; tracking the target in the sequence images, and marking a central point of the target to be identified in each image in the sequence images; segmenting the to-be-recognized target from each image according to the central point, and performing spatial reconstruction on the to-be-recognized target according to a segmentation result to obtain three-dimensional information of the to-be-recognized target; and obtaining the posture of the target to be recognized according to the three-dimensional information. According to the method and the device, the problem of low accuracy of posture recognition of the target in the video in the prior art is solved, so that the accuracy of recognizing the posture of the target in the video image is improved to a certain extent.