30results about How to "Accurate recognition rate" patented technology

Disclosed is a method and device for human face detection and recognition used in a preset environment. The human face detection device comprises a camera module, a synchronous image acquiring module, a human face detection module, a human face recognition module and a member face database module. The synchronous image acquiring module is used to acquire synchronously images photographed by the camera module. The human face detection module is used to detect if any human facial feature is presented in the image. If the human facial feature is confirmed, the image is transferred to the human face recognition module to extract the human feature from the image. Then, the extracted human facial feature is compared with member face data stored in the member face database module so that a recognition result which shows a person corresponding to the image is a legal or illegal member is generated.

The invention provides an underwater target feature extraction method based on a convolutional neural network (CNN). 1, a sampling sequence of an original radiation noise signal is divided into 25 consecutive parts, and each part is set with 25 sampling points; 2, normalization and centralization processing are carried out on a sampling sample of the j-th segment of the data signal; 3, short-time Fourier transform is carried out to obtain a LoFAR graph; 4, a vector is assigned to an existing 3-dimensional tensor; 5, an obtained feature vector is input to a fully-connected layer for classification and calculation of error with label data, whether the loss error is below an error threshold is tested, if the loss error is below the error threshold, network training is stopped, and otherwise, step 6 is entered; and 6, a gradient descent method is used to carry out parameter adjustment layer by layer on the network from back to front, and shifting to the step 2 is carried out. Compared with the traditional convolutional neural network algorithms, the method of the invention carries out a weighted operation of spatial information multi-dimensions on a feature graph layer to compensate for a defect of spatial information losses caused by one-dimensional vectorization of the fully-connected layer.

An optical reading apparatus and optical character recognition processing apparatus operating in conjunction with a magnetic ink character reading apparatus reduce the time in reading a string of characters formed in a line on a processed medium. This is done in the optical reading and recognition operations by selectively using a broad recognition area that allows for variation in character positions and a narrower recognition area where the probability of the desired character string being present is high depending on past results. An extracted image containing the character string is acquired from scanned image data and the recognition process is run. If recognition succeeds, the next matching area is set to a relatively narrow predicted range and the recognition process is applied to the predicted range. If character recognition succeeds within a specified distance, the next matching area is set to a relatively narrow predicted range. If character recognition does not succeed within the specified distance, the next matching area is set to the full width of the extracted image.

The invention provides an RFID (Radio Frequency Identification) reader with multiple antennae working simultaneously, and a radio frequency data signal identification method of the same. The RFID reader comprises an anti-collision unit, more than two radio frequency units and more than two antennae, wherein the radio frequency units are respectively connected with the anti-collision unit, and each antenna is connected with one radio frequency unit. The RFID reader can be used for realizing simultaneous working of multiple antennae of RFID, and solving the problem that the existing multi-antenna RFID system is prone to identification omission when only one antenna can work at the same time.

A magnetic ink character reading apparatus, magnetic ink character reading method and program, and a POS terminal apparatus reduce recognition errors and thereby improve the read rate. The magnetic ink character reading apparatus reads a text string of magnetic ink characters using both a magnetic reading mechanism and optical reading mechanism to obtain magnetic ink character recognition (MICR) results and optical character recognition (OCR) results, which are compared. The OCR process is repeated if the results differ. The positions of the read character blocks are compared to find character blocks that are offset perpendicularly to the base line of the magnetic ink characters, and the OCR process is repeated. The area to which the OCR process is applied again is near the position of the offset character block corrected in the direction perpendicular to the line of magnetic ink characters to be in line with the character blocks for which the MICR result and OCR result were the same.

The invention belongs to the technical field of artificial intelligence, and discloses a behavior recognition method based on a local articulation point trajectory space-time volume in a skeleton sequence, and the method comprises the steps: extracting the local articulation point trajectory space-time volume from inputted RGB video data and skeleton articulation point data; extracting image features by using a pre-training model based on the RGB video data set; constructing a codebook for each different feature of each joint point in the training set and encoding the codebook, and connectingthe features of the n joint points in series to form a feature vector; and performing behavior classification and recognition by using an SVM classifier. According to the method, manual features and deep learning features are fused, local features are extracted by using a deep learning method, and fusion of multiple features can achieve a stable and accurate recognition rate. According to the invention, the 2D human body skeleton estimated by the attitude estimation algorithm and the RGB video sequence are used to extract the features, the cost is low, the precision is high, and the method hasimportant significance when applied to a real scene.

The invention provides a facial expression migration method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a real facial image; inputting the real face image into a pre-trained face expression recognition network model, and outputting an action unit coefficient corresponding to a face expression in the real face image, wherein the face expressionrecognition network model is obtained through training of a virtual face data set and a real face data set, and the action unit coefficient is used for representing face data when a face is in different expressions; and driving the face of the virtual object to make a corresponding facial expression according to the action unit coefficient so as to realize facial expression migration, i.e., the facial expression recognition network model in the embodiment of the invention is obtained through joint training of the virtual facial data set and the real facial data set, so that the recognition rate is more accurate, the accuracy of facial expression recognition is improved, and the virtual object can make more realistic facial expressions.

The invention relates to a novel video tracking method based on self-adaptive partitioning, fully considers difference of pixel values inside a target area, and the video tracking method based on particle filtering realizes efficient and self-adaptive video tracking and improves accuracy and adaptivity of video tracking. For the problems of shielding, interference and the like which occur in video tracking, the video tracking method provided by the invention can maintain a relatively accurate recognition rate, fully considers content information in a video and image, and adaptively adjusts a corresponding partitioning strategy according to characteristics of a tracked target area, thereby achieving a high-intelligence and high-accuracy video tracking effect.

The invention provides an intelligent detection system for monitoring the real-time state of an iron tower and a method thereof. The system comprises an iron tower, and further comprises an iron towerintelligent monitoring platform, an intelligent monitoring subsystem and a user interaction interface, wherein four types of sensors are installed on the iron tower, and the four types of sensors comprise an angle sensor for monitoring the tilt angle of the iron tower, a vibration sensor for monitoring the vibration of the iron tower, a Hall current sensor for monitoring the lightning current andan electric leakage detection sensor for detecting electric leakage; a gateway with a gateway monitoring management and configuration management system is also deployed on the iron tower; and the gateway deployed on the iron tower performs collection and transmission to the iron tower intelligent monitoring platform through an Internet of Things communication protocol. The data uploaded by the sensors arranged on the iron tower can be intelligently analyzed, and the state of the iron tower can be monitored and judged in real time; and through video acquisition, whether the iron tower has a dangerous situation is judged in real time, and compared with manual monitoring, the intelligent monitoring has a more accurate recognition rate.

The invention belongs to the field of application software detection of Android terminals, and particularly relates to an Android application digital certificate verification vulnerability detection system and method, and the system comprises a static detection module, a dynamic detection module and an intermediary agent module. The static detection module is used for discovering potential applications with digital certificate verification vulnerabilities according to static code characteristics of vulnerability applications; the dynamic detection module is used for dynamically executing application triggering vulnerability codes; the intermediary agent module is used for initiating intermediary attacks and trying to decrypt HTTPS flow so as to confirm whether digital certificate verification vulnerabilities really exist in applications or not, and the method makes up for the defects of false alarms caused by single use of static detection and low efficiency caused by single use of dynamic detection through the mode of combining dynamic detection and static detection, effective detection of the application is achieved, and the problems of low efficiency of manual auditing, high cost of large-scale market-level application detection and the like are solved.

The invention provides a document review proofreading method and device, and belongs to the technical field of similarity matching. The document review proofreading method comprises the steps: obtaining a to-be-proofreading document, and splitting the to-be-proofreading document according to a preset rule, so as to obtain a proofreading template; obtaining annotation information for terms in a term directory of the to-be-proofreading document, and splitting the annotation information according to the predetermined rule to obtain a to-be-proofreading text; comparing the proofreading template with the to-be-proofreading text corresponding to the proofreading template; and when the proofreading template is consistent with the content of the to-be-proofreading text corresponding to the proofreading template, determining that the to-be-proofreading text is correctly labeled. According to the document review proofreading method, the efficiency of document review and proofreading is improved,and the proofreading cost is reduced.

The invention provides a scanning recognition system, which comprises a scanning unit, a weight acquisition unit, an alarm unit and a storage and control unit, and is characterized in that the storage and control unit is set to automatically compare pre-stored book basic weight information G with book real-time weight information G'acquired by the weight acquisition unit; and according to the forward or reverse type of the difference between the book basic weight information G and the book real-time weight information G ', judging that the book damage category is defect or wet damage, and when the book damage category is judged to be wet damage, further identifying the book wet damage degree as one of different wet damage levels according to the difference between the book basic weight information G and the book real-time weight information G'. And the signal of the alarm unit is controlled according to the identified moisture loss level. The invention also provides a scanning identification method based on a threshold value. Wet damage identification alarm and disposal of the book are achieved in book scanning through the weight detection and comparison principle, large-scale hardware development and upgrading of book scanning equipment are not needed, and the method is efficient, accurate and low in cost.

The invention discloses a disturbance information judgment method and system based on NER and NLU, and the method comprises the steps: obtaining first text information; performing natural language understanding processing on the first text information to obtain natural language understanding intention list information; performing named entity identification on the first text information to obtain word slot content information; obtaining first result information according to the natural language understanding intention list information and/or the word slot content information; judging whether the first result information contains harassment information or not; if the first result information contains harassment information, obtaining first mark information; and marking the first text information as harassment information according to the first marking information. The technical problems that in the prior art, time and energy are greatly consumed, quick response cannot be achieved, pertinence is not high, and low-cost updating cannot be achieved are solved.

The invention provides a garbage classification system and method based on garbage recognition, and relates to the field of garbage classification treatment. According to the garbage classification system based on garbage recognition, through the combination of a data crawler, an image recognizer and a semantic recognizer, pictures related to recoverable garbage, harmful garbage, kitchen garbage and other garbage on the Internet are crawled down, the pictures are labeled through the image recognizer, real object nouns are reserved in a garbage classification label library through a semantic recognizer, the garbage classification label library is established, and labels of images uploaded by a user are retrieved in the garbage classification label library, so that garbage is recognized and classified, training images do not need to be labeled manually, manual guidance in material screening is avoided, and blind areas can be identified; and meanwhile, through a word meaning similarity comparison function of the semantic recognizer, labels with different words but the same word meaning can be matched, so that the generalization ability of the system is greatly improved.

The invention provides a reservoir dynamic monitoring system and method based on multi-source data, and belongs to the reservoir dynamic monitoring technology, and the system comprises a data acquisition module, a multi-source remote sensing data sample module, a deep learning module, a reservoir dynamic text updating module, a reservoir dynamic monitoring module and a cloud platform. According to the invention, the method includes: constructing a nationwide total-factor reservoir information database; performing reservoir target detection by using an object-oriented classification method in combination with a deep learning technology, and endowing reservoir attribute information; updating a reservoir information database according to a remote sensing monitoring method and a ubiquitous network monitoring method. The nationwide reservoir dynamic active monitoring method can actively discover, track and lock reservoir construction and management hot spots and key targets, achieves automatic extraction of deep reservoir information, and provides great convenience for people to use water conservancy information.