461results about How to "Avoid double counting" patented technology

The invention relates to a statistical device and a detection method for television audience ratings based on human face detection, and the device includes a dual camera, an infrared lamp, an infrared lamp light filtering device, a set-top box and a background server, wherein, the dual camera, the infrared lamp and the infrared lamp light filtering device are in a parallel connection, the dual camera is connected with another set-top box, and the set-top box is connected with the background server in a wired or a wireless mode, and the set-top box is also connected with the television set. The invention has the advantages that identity and watching state are recognized and confirmed by biological characteristics; number of people for watching specific advertisement in each time period, preference degree as well as age, gender, level of living of watcher are counted, detected human face is in real-time tracking, thereby avoiding multiple counting and improving accuracy of television audience rating.

The invention provides a method for quickly invoking program instructions, comprising that coding character string input by the user is received; in a prearranged mapping table, a triggering condition is queried according to the coding character string or the content which is converted by the coding character string; if records complying with the matching exist, the instruction is started to trigger an interface and unfold corresponding information; the triggering information of the user is received and corresponding instruction invoking information is gained; the instruction invoking information is sent to corresponding interfaces to realize the invoking of corresponding program instructions. As the technical proposal of the invention is based on keyboard input, the instruction inlet is not needed to be searched in the screen, thus avoiding the problems of low efficiency of positioning means such as menu, button, icon, etc., which are based on graphic interfaces; secondly, the technical proposal of the invention can support the operation system instruction and application program instruction at the same time, thus unifying the inlet of computer program instructions and improving the efficiency of the instruction invoking.

In order to improve the efficiency of vehicle positioning to meet the real-time requirements for vehicle type identification, the present invention provides a vehicle type identification method and system based on a deep neural network. The method comprises: integrating candidate target extraction and target identification into a network, and using end-to-end detection/identification methods to integrate feature extraction, target location, and target detection into a single network. According to the method and system provided by the present invention, target extraction t is no longer extracted from the original image, but is extracted from the high-dimensional feature map with very small dimensions by using the reference point and multi-dimensional coverage manner, and at the same time, the method for sharing the deep convolutional network parameters is used in the feature extraction layer, so that repetitive calculation of features is avoided, the identification efficiency is greatlyimproved, 20fps is reached in reality, the effect of the processing efficiency of a single server reaches 2 million sheets/day, and the requirement for real-time vehicle type identification is satisfied.

The embodiment of the invention discloses an internet advertisement anti-spamming method and an internet advertisement anti-spamming system, so as to solve the problem that the statistical result of information, such as impressions or click rate of an internet advertisement is inaccurate caused by incapability of distinguishing spamming log data from non-spamming log data due to the fact that the spamming log data is mingled with all log data. The method comprises the steps of reading and partitioning various log data through a first single-thread; counting data required by various predetermined spamming rules in the log data partitioned by the first single-thread through a first multi-thread; reading and partitioning various log data through a second single-thread; judging whether the log data partitioned by the second single-thread is spamming log data or not through a second multi-thread according to the predetermined spamming rules and the data required by the predetermined spamming rules; and marking the log data partitioned by the second single-thread according to a judging result. The log data is only required to be counted for one time, so that the repeated counting of log data in the same dimensionality is avoided.

The invention discloses an incremental learning and face recognition method based on locality preserving nonnegative matrix factorization (LPNMF), and relates to the technical field of pattern recognition. The incremental learning and face recognition method is essentially a face recognition method based on on-line learning. The method comprises the step (a) of face image preprocessing, the step (b) of initial sample training, the step (c) of incremental learning and the step (d) of face recognition. The incremental learning and face recognition method can be applied to a linear face recognition system and a non-linear face recognition system, keeps local structures of original space of face images, greatly improves recognition rate, and can be actually applied in an on-line mode.

The invention discloses a method and equipment for common detection of human faces and human bodies. The method comprises the steps that standard data is obtained, wherein the standard data comprises position frame information for labeling the positions of the human faces and the human bodies of all pedestrians; a common recognition model is corrected through the position frame information in the standard data, wherein the common recognition model is generated based on Faster RCNN; common detection of the human faces and the human bodies is carried out on images in a video to be recognized through the corrected common recognition model to output real-time structuralization information, wherein the structuralization information comprises human face information and human body information of the pedestrians at the same time. Thus, efficient simultaneous recognition is achieved, resources are saved, practicality is improved, and the corresponding relation between the human faces and the human bodies is guaranteed.

The invention discloses a method for positioning a plane centroid of a pipe orifice image of a condenser. The method comprises the following steps of: (1) calibrating a camera; (2) controlling a hand-eye mechanism of a robot to move along an optical axis of the camera, continuously shooting gray aerial views of a pipe plate area to be cleaned, selecting a clear image from the gray aerial views, pre-processing the clear image to obtain a plurality of binary images of which pipe orifices and backgrounds are in black-white contrast, and distributing the pipe orifices in the pipe plate area to becleaned in a horizontal and longitudinal staggered mode; (3) drawing a column pixel gray value accumulated sum curve S1 and a line pixel gray value accumulated sum curve S2, counting horizontal coordinates of a local minimal value point set at a column trough in the S1 and longitudinal coordinates of the local minimal value point set at a line trough in the S2, and thus obtaining a centroid roughcoordinate set; and (4) eliminating falsified centroids to roughly position the centroid in a multi-target mode. The method has the advantages of high accuracy and small calculation amount.

The invention provides an end-to-end tendency scene character detection and recognition method and an end-to-end tendency scene character detection and recognition system. The method comprises the steps: collecting pictures marked with object types, constructing a training data set and a test data set, and carrying out the preprocessing of the data sets for training a feature extraction network; collecting pictures marked with character positions, constructing a training data set and a test data set, and preprocessing the data sets to train a network of a character detection part; pictures marked with character positions and character contents at the same time are collected, a training data set and a test data set are constructed, and corresponding picture preprocessing is carried out to train a neural network of a character recognition part; inputting the picture into a convolutional neural network; extracting a shared convolution feature map through a feature extraction network; andmapping predicted character position coordinates to the shared convolution feature map by the character detection part maps by using the shared convolution feature map, cutting off a feature map blockcorresponding to the character part in the image and converting the feature map block into a feature sequence; finally decoding the feature sequence into a readable character sequence. Since the convolution characteristic spectrum is only calculated once, the intermediate redundancy process is avoided. The speed of the whole scene character detection and recognition system is improved.

The invention relates to a method for emitting detector which can select the detected target via swing-by mechanism, belonging to the technique of deep space detecting rail transfer. It can solve the problems of approachability evaluation of detected target with long semi long axis or large eccentric rate. The inventive method comprises: calculating optimized dual impulse transfer track; selecting swing-by track type; and assembling the swing-by tracks. First calculating the optimized dual impulse transfer track from the target star to emitting star; using the emitting star as swing-by star; using the parameter of dual impulse transfer at the emitting star as the match parameter in swing-by flying; using the heliocentric large-ellipse orbit whose period is several times of the revolution period of emitting star and the deep space device at aphelion, to search for the emitting parameter of said match condition. The invention guides in the swing-by mechanism to realize the expansion of former dual impulse transfer, to improve the approachability of the target with long semi long axis or large eccentric rate.

The embodiment of the invention discloses a speed forecasting method which comprises the following steps: calculating section lengths of all planned sections in a processing path and maximum forecasting speed of section end points corresponding to the section lengths; saving the section lengths of all the planned sections and the calculation results of the maximum forecasting speed of the section end points corresponding to the section lengths to finish the preprocessing phase; and performing forecasting processing to each planned section according to the calculation results. The embodiment of the invention further discloses a numerical control device and a numerical control system. By virtue of the methods, the safety and the reliability for processing of the numerical control machine tool is ensured, and the processing efficiency and the processing quality are improved.

The embodiment of the invention discloses a method for building a user image. The method includes the steps that an original behavior log of the user is obtained; the original behavior log is preprocessed to generate a target behavior log of the user; the target behavior log is analyzed to generate the latest behavior sequence of the user; a historical behavior sequence of the user is called; the latest behavior sequence and the historical behavior sequence are fused, and an attribute label of the user is obtained through calculation; the user image of the user is built according to the attribute label. By using the historical behavior sequence of the user, repeated calculation can be avoided in the process of user behavior sequence generating which consumes most resources, then hardware resources are saved, and the building efficiency of the user image is improved. Correspondingly, the embodiment of the invention further discloses a device for building the user image.

The invention discloses a permanent magnet synchronous motor model prediction control method comprising the following steps: (1) converting an acquired three-phase current to an alpha-beta coordinate system at a current moment k; (2) predicting the current value at a moment k+1 and a current reference value at a moment k+2 according to the current value of the alpha-beta coordinate system; (3) calculating the voltage control vector reference value at the moment k+1 according to the current value at the moment k+1 by taking the current reference value at the moment k+2 as a control objective; (4) analyzing out the angle corresponding to the voltage control vector reference value at the moment k+1, determining a sector to which the angle belongs, and extracting candidate voltage control vectors from the sector; and (5) substituting the candidate voltage control vectors into an objective function, and selecting the candidate voltage control vector corresponding to the minimum objective function value as an optimal voltage control vector. A lot of repeated calculation is avoided, the voltage vector is more accurate, the torque ripple of motors is reduced, and delay of the microprocessor due to calculation is offset.

The invention discloses a traffic flow visual inspection method. Traffic scene image sequences are acquired through electronic monitoring in real time, a traffic scene weather condition environment is sensed by an illumination intensity sensor, a temperature sensor and a humidity sensor, daytime/night, fine day/rainy day/normal day and other weather conditions are judged, and illumination and shadow pretreatment is performed correspondingly. Running distances are restrained according to traffic rules, meanwhile lane departure phenomenon caused by overtaking and passing-by in the actual vehicle running is considered, double virtual lines are arranged at the same horizontal positions of all of lanes within a monitoring range in images, vehicle positions are quickly detected and positioned by utilizing double-template matching convolution in a double virtual line detecting region, 'one-to-more' and 'more-to-one' phenomena are eliminated, and wrong detection and wrong judgment are decreased. Intervals of vehicles in the horizontal direction and the vertical direction are judged and identified, vehicle target positions are restrained according to the horizontal and vertical position information of the vehicles and correctly positioned, normally running vehicles are counted, traffic flow statistic is performed, and the problem of inaccurate traffic flow counting is solved. The traffic flow visual inspection method has high detecting accuracy and good anti-interference performance and real-timeliness.

The invention provides a passenger flow counting method based on deep learning in a vertical visual angle. According to the method, passenger flow statistics is performed in the vertical visual angle. Compared with an oblique photographing visual angle, the visual angle realizes easier coping with conditions such as market, supermarket and subway with high passenger density. The method provides head and shoulder detection by means of deep learning. Strong learning capability of deep learning is utilized. Background modeling and foreground extraction on a video are not required, and passenger cutting on the foreground is not required. Head information and shoulder information can be more accurately detected with higher robustness. A deep convolutional characteristic is utilized for performing matched tracking. Compared with manually designed characteristics such as HOG and LBP, the deep convolutional characteristic has better expression capability and can better cope with various scenes. The passenger flow counting method directly use the deep convolutional characteristic of a certain layer for matching, thereby preventing repeated characteristic calculation and realizing high time saving effect.