1434results about How to "Reduce false detection rate" patented technology

The invention discloses a remote obstacle detection method based on laser radar multi-frame point cloud fusion. A local coordinate system and a world coordinate system are established, an extraction feature point of each laser point is calculated on an annular scanning line of the laser radar according to the original point cloud data under the local coordinate system, and the global pose of the current position relative to the initial position and the de-distortion point cloud in the world coordinate system are obtained through inter-frame feature point matching and map feature point matching; the de-distortion point clouds of the current frame and the previous frame are fused to obtain more compact de-distortion point cloud data, which is unified to the local coordinate system, then projection is performed on two-dimensional grids, and an obstacle is screened according to the height change features of each two-dimensional grid. According to the method in the invention, the problem that the detection rate of the remote barrier caused by sparse laser point clouds is low is solved, the remote barriers can be effectively detected, the error detection rate and the leak detection rateare low, and the system cost can be greatly reduced.

The invention relates to a condition monitoring data stream anomaly detection method, in particular to a condition monitoring data stream anomaly detection method based on an improved gaussian process regression model. The problem that an existing method for processing monitoring data stream anomaly detection is poor in effect is solved. The method comprises the steps that firstly, the historical data sliding window size is determined; secondly, the types of a mean value function and a covariance function are determined; thirdly, the hyper-parameter initial value is set to be the random number from 0 to 1; fourthly, q data closest to the current time t are extracted; fifthly, the gaussian process regression model is determined; sixthly, prediction is conducted by means of the nature of the gaussian process regression model; seventhly, PI of normal data at the time t+1; eighthly, monitoring data are compared with the PI; ninthly, whether the real monitoring data need to be marked to be abnormal or not is judged; tenthly, beta (xt+1) corresponding to the monitoring value at the time t+1 is calculated; eleventhly, the real value or prediction value and the t+1 are added into DT; twelfthly, new DT is created. The condition monitoring data stream anomaly detection method based on the improved gaussian process regression model is applied in the field of network communication.

The invention discloses a LTE PDCCH (Long Term Evolution System Physical Downlink Control Channel) blind inspection control method and device. According to the device, the PDCCH blind inspection times can be reduced by limiting polymerization grade ranges to be inspected in a certain range through a polymerization grade range calculation unit, a candidate PDCCH number calculation unit, an uplink/downlink scheduling state maintenance unit, a searching space state maintenance unit and a candidate PDCCH position calculation unit; and the repeated inspection of the same CCE (Control Channel Element) initial position in the same polymerization grade can be prevented by limiting the number of candidate PDCCH corresponding to each polymerization grade in a certain range; the inspected state of each CCE in a searching space is maintained to avoid the repeated inspection of CCE positions of detected DCI (Downlink Control Information) in different polymerization grades; and uplink/downlink scheduling states are maintained to reduce the inspection of uplink/downlink scheduling PDCCH number. By using all the units, the computation quantity and the searching time can be decreased; and the error inspection probability and the system power consumption can be reduced.

A water body information automatic extraction method for a multi-spectral image of a remote sensing satellite comprises the steps of (1) implementing normalized difference water index (NDWI) extraction on apparent reflectivity data of the multi-spectral image of the domestic remote sensing satellite, and forming an NDWI image; (2) implementing maximum between-cluster variance algorithm based global threshold segmentation initial water body information extraction on the NDWI image; (3) implementing local buffer area expansion and threshold segmentation on the initial water body information, and obtaining a stable water body unit result set; and (4) according to elevation data, obtaining slope and shadow information, removing a shadow from the water body unit result set, and finally finishing the water body information extraction. According to the method, by adopting a computer automatic interpretation method and utilizing a thought of from global segmentation to local iterative segmentation, the NDWI is subjected to water body information extraction in a process manner; and the method is high in extraction precision and easy in engineering.

The invention discloses an improved visual background extraction based movement target detection method. The improved visual background extraction based movement target detection method comprises the following steps of establishing a background model for every pixel point of a video image; calculating the similarity of the current pixel point and the background model; classifying into backgrounds if the current pixel point and the background model are similar and classifying into foregrounds if not; selecting whether to update the background models and display a movement target or not according to the times of continuous judgment of foregrounds. The improved visual background extraction based movement target detection method can remove Ghost caused by error judgment of the backgrounds into the foregrounds.

The invention relates to a robust ear detection method which improves an AdaBoost arithmetic and belongs to the technical field of image mode identifying. The invention is characterized by proposing an ear detection method with excellent performances under a complex underground. The invention proposes four anisomerous Haar-like corner characteristics which are used for describing the grayscale changes on the partial areas of the ears; a policy of subsection selection is adopted for selecting the best sorting threshold of the Haar-like characteristics, thus reducing the sample training time; the weight of a weak sorter is modified for reducing the mistaken detection rate of the sorter; the threshold HW is set and eliminated according to the distribution change of the sample weight in the training, thereby preventing an over-studying phenomenon from being generated and leading the miss-detection rate and the mistaken direction rate of the ear detection to be reduced; besides, the invention also provides a single-ear detection policy for leading both the defection efficiency and the detection effect to be improved. The excellent performances of the robust ear detection method are shown on a PC machine and a DSP.

The invention discloses an improved image edge detection method. The improved image edge detection method comprise steps of S1, performing smoothing processing on an image and using an improved media filter to suppress noise, S2, obtaining a difference between a horizontal direction and a perpendicular direction through first-order-partial derivatives of directions of x,y,45 degrees and 135 degrees and thus obtaining a gradient amplitude and a gradient direction, S3, performing non-maximum value inhibition on the gradient amplitude, S4, using a gradient histogram to solve a high threshold and a low threshold and then using a double-threshold algorithm to perform edge detection on the image, and S5, performing sharpening processing and connecting the edges to obtain a final edge image. The improved image edge detection method use weighted media filtering to replace gauss filtering, uses the partial derivatives of four directions, uses the gradient histogram to determine the high threshold and the low threshold, reduces detection errors, improves detection accuracy, and makes the counter of the edge image more clear and continuity better.

The invention belongs to the video monitoring technical field, in particular relating to a method and a system for eliminating shadows in video monitoring. The method comprises the following steps of collecting a plurality of frames of initial images and setting a background model to obtain a background image; having acquired video stream images used as input images respectively, detecting foreground pixel points and distinguishing moving pixel points from static pixel points to obtain a binary foreground image; detecting and compensating shadow points in the foreground image, and outputting the foreground image which is processed with a shadow compensation; and updating the background image by using the compensated foreground image and a current inputted image, and repeating the operations. The invention uses a YUV image captured by a camera to directly perform motion detection and shadow detection, avoids a color space transformation or an edge detecting operation in the prior shadow detection method, further improves operating speed for shadow detection, and is advantageous for real-time application.

The invention discloses a public transport vehicle passenger flow statistical method and system. Concretely, two cameras are respectively installed at the front and rear doors of a bus. The release lines of the front and rear doors of the bus and a fitting function of the camera height and the head size are determined before processing images acquired by the cameras. After a door opening signal is received, preprocessing, head detection, head tracking and head movement track analysis are performed on the images acquired by the cameras in turn, and finally passenger behaviors are analyzed according to head movement tracks, the number of bidirectional passenger flow of the corresponding doors is acquired and the number of bidirectional passenger flow of the whole bus is acquired through statistics of the number of bidirectional passenger flow of the front and rear doors of the bus. After a door closing signal is received, processing of the images acquired by the cameras is stopped, and the problem of influence on the statistical result caused by abrupt change of illumination generated by movement of the bus can be reduced. The method has advantages of high statistical accuracy so that reliable supporting can be provided for intelligent scheduling of the bus.

The invention discloses a vehicle detection method based on laser and vision fusion. The method comprises the following steps of 1) acquiring target detection information for an input image and laserpoint cloud; 2) performing optimal matching on the images of the front frame and the rear frame and the point cloud detection frame, and establishing a tracking sequence of an image and point cloud detection target; 3) fusing the tracking sequences of the image and the detection frame thereof and the tracking sequences of the point cloud and the detection frame thereof; 4) classifying all the target detection boxes, outputting a fusion list, and outputting a fusion result; and 5) obtaining the accurate position of the surrounding vehicle relative to the vehicle in the current frame, reading the next frame of image and the point cloud data, circulating the steps 1) to 5), and outputting a fusion detection result. According to the method, on the basis of point cloud and image target detection, the detection result is subjected to information tracking, the detection result is optimally matched, and the fusion result is preferentially input into the final fusion list, so that compared witha single sensor target detection method, the target detection precision is improved, and the false detection rate is reduced.

The invention discloses a network anomaly traffic monitoring method, belonging to the technical filed of information security. The network anomaly traffic monitoring method comprises the steps of: capturing a network data stream passing by; according to generation time of the network data stream, selecting n network data streaming data which are closest to the current time, wherein n is determined according to the calculating capacity of a system; training the captured n network data streaming data as input of a relevance vector machine, establishing a data model; and monitoring the current network traffic data according to the data model. By the method and the device, the classifying monitoring precision can be improved so that anomaly traffic is monitored more rapidly and effectively, and lower false drop rate and fallout ratio are ensured.

The invention discloses a target detection method in a Codebook dynamic scene based on superpixels. The method is characterized by comprising the following steps that (1) a superpixel partition method is used for partitioning video frames, K superpixels are obtained by partitioning; (2) a Codebook background modeling method is used, a Codebook is established for each superpixel partitioned in the step (1), each Codebook comprises one or more Codewords, each Codeword has the maximin threshold values during learning, the maximin threshold values are detected, background modeling is completed; (3) after background modeling is completed, currently-entering video frames are subjected to target detection, if a certain pixel value of the current frames accords with distribution of the background pixel values, the certain pixel value is marked as the background, otherwise, the certain pixel value is marked as the foreground; finally the current video frames are used for updating the background model. The method solves the problems that a traditional Codebook background modeling algorithm is large in calculated amount and high in memory requirement, and established Codewords are not accurate are solved, target detecting accuracy and speed are improved, the requirement for real-time accuracy is met, and accordingly the requirement for intelligent monitoring in real life is met.

The invention belongs to the field of computer security, and relates to a method for detecting web page Trojan horse based on program execution characteristics, which comprises the following steps: using web crawlers to capture source codes of a web page; then obtaining a recognizable script program through multilevel decoding; carrying out disassembling processing on the script program to obtainassembled source codes while reserving the script program; then, judging whether a large number of filled invalid instructions, calling system level functions and obvious URL links exist in the sourcecodes; and finally detecting whether the Trojan horse exists in the web page through the assembled source codes in a deep level. Because most of the prior web pages with the Trojan horse are embeddedwith ShellCode, to execute the ShellCode in the web pages in a local computer, system vulnerability is needed to realize buffer overflow and enable the program to skip onto the ShellCode code segment. Thus, only by analyzing the condition of executing the ShellCode, and analyzing the source codes according to the execution characteristics, whether the web page to be detected is the web page Trojan horse can be quickly detected.

The embodiment of the invention discloses a skin-tone detection method and a skin-tone detection device. The method comprises that: skin-tone pixels collected by a training specimen are clustered in a color space to obtain at least one skin-tone chroma class; a candidate skin-tone region in the training specimen is extracted, the distance between a chroma mean value of the candidate skin-tone region and the center of the skin-tone chroma class is computed, the training specimen is fallen under the skin-tone chroma class with the smallest distance to obtain a training subset corresponding to the skin-tone chroma class; the skin-tone probability distribution and the non skin-tone probability distribution of each training subset are statisticized to obtain a skin-tone probability model that each skin-tone chroma class corresponds to; an image to be detected is received, the candidate skin-tone region of the image to be detected is extracted, the distance between the chroma mean value of the candidate skin-tone region and the center of the skin-tone chroma class is computed, pixels in the image to be detected are made skin-tone discrimination according to the skin-tone probability model that the skin-tone chroma class with the smallest distance corresponds to, and the pixels discriminated as the skin-tone form a skin-tone region image. With the invention, the miss detection rate or the error detection rate of the skin-tone can be decreased.

The invention discloses a left object and lost object real-time detection method based on an embedded system. The left object and lost object real-time detection method based on the embedded system comprises the following steps of obtaining video data, extracting a suspicious static target object area on the basis of backgrounds of images, to be detected, of long-and-short period Gaussian mixture model learning video data, and obtaining a target foreground binary image; carrying out accumulation timekeeping on the retention time of the suspicious static target object area according to the target foreground binary image, carrying out triggering to give an alarm if the retention time exceeds a preset threshold in a preset alarm time, and marking size dimension information and specific position information of a suspicious static target object on an original image; determining whether the suspicious static target object area contains a detected target or not through analysis of the shaking conditions of the edge of the suspicious static target object area, and finally obtaining a rectangular target area; distinguishing a left object and a lost object in the rectangular target area containing the detected target, and giving an alarm. The left object and lost object real-time detection method based on the embedded system is low in operation overhead, can be implemented in an embedded SOC leading end, and the requirement for real-time detection can be met.

The invention relates to a detection sorting device for steel balls The detection sorting device comprises a rack, a feed mechanism, an unfolding mechanism, a stock stop, a detecting mechanism, a sorting mechanism and a material collecting mechanism, wherein the feed mechanism comprises a material storing box, a material discharging hopper and a material discharging pipe; the unfolding mechanism comprises a material transporting guide rail, the material transporting guide rail is a V-shaped linear guide rail, the material discharging hopper is connected with an initiating end of the material transporting guide rail through the material discharging pipe, and the detecting mechanism comprises a light source assembly and a camera assembly; the light source assembly is arranged on the material transporting guide rail in a sleeved mode, the sorting mechanism and the material collecting mechanism are arranged on the tail end of the material transporting guide rail, and the separate collection of defect steel balls and qualified steel balls are realized through the sorting mechanism and the material collecting mechanism. The detection sorting device provided by the invention has the advantages that the automatic feed, detection and sorting of the steel balls are realized, a detection process is that two-dimensional motion of a traditional unfolding disc is changed into single one-dimensional rolling of one-sided steel balls, the detection efficiency is high, a false drop ratio and an omission ratio are low, and the steel balls are not magnetized or the surfaces of the steel balls are not damaged in a detection process.

The invention discloses an automation detection device of electrical connectors. The automatic detection device comprises a shaking part, a detection part and a packaging part, wherein the shaking part is used for neatly distributing and conveying electric connectors to be detected, the detection part is used for conveying the electrical connectors from the feeding direction to the discharging direction, a CCD (Charge Coupled Device) detection device is arranged on a conveying path so that the flatness of terminals in insertion holes of the electrical connectors is automatically detected, finished products with unqualified flatness are automatically selected out, and finished products with qualified flatness are automatically conveyed to the packaging part; the packaging part can be used for attracting the plurality of neatly arranged electrical connectors together to a packaging disc by using a magnetic attraction force so that the electrical connectors are conveniently and automatically packaged. Therefore, according to the automatic detection device, the automation operation of the electrical connectors from detection to package is completed, the detection efficiency can be effectively increased, and the labor cost and the false detecting rate are reduced.

The invention discloses a mobile platform ground movement object detection method including the steps as follows: (1) respectively calculating SWIFT characteristic description of a (n-m)th frame image, an nth frame image and a (n+m)th frame image of an image sequence; (2) by taking the nth frame image as a reference, respectively registering the (n-m)th frame image and the (n+m)th frame image to obtain registration images; (3) respectively conducting space multiscale movement significance tests on the two registration images with the nth frame image; (4) calculating a difference absolute value image of the nth frame image and the (n-m)th frame registration image, and the difference absolute value image of the nth frame image and the (n+m)th frame registration image; (5) respectively calculating a difference image of the (n-m)th frame and the nth frame as well as a difference image of the nth frame and the (n+m)th frame; (6) mixing the two difference images to obtain a three-frame difference image and then conducting binaryzation; and (7) conducting morphological processing on a binary segmented image and then conducting region marking so as to obtain a final detection result image. The mobile platform ground movement object detection method solves the background compensation problem under the condition of a moving platform and has very good robustness on the light change and background interference in a scene as well as image deformation caused by platform movement.

The invention relates to a pedestrian detecting method based on improved HOG feature and PCA (Principal Component Analysis). The method comprises the following steps: extracting a sample feature by using a feature extracting algorithm for HOG feature cascaded PCA in a concentrated area of gradient information of a pedestrian in a sample image; training a SVM classifier by utilizing the feature extracted from the sample; and utilizing a feature extracting method to extract a feature vector for a detected sample and utilizing the trained and obtained SVM classifier to detect the pedestrian. The invention provides a pedestrian detecting method based on improved HOG feature and PCA, with the capabilities of efficiently reducing the training speed and reducing the false detecting rate and missing report rate.