1614 results about "Track algorithm" patented technology

The present invention is directed to generating audience analytics that includes providing a database containing a plurality of user input pattern profiles representing the group of users of terminal device, in which each user of the group is associated with one of the plurality of user input pattern profiles. A clickstream algorithm, tracking algorithm, neural network, Bayes classifier algorithm, or affinity-day part algorithm can be used to generate the user input pattern profiles. A user input pattern is detected based upon use of the terminal device by the current user and the user input pattern of the current user is dynamically matched with one of the user input pattern profiles contained in the database. The current user is identified based upon dynamic matching of the user input pattern generated by the current user with one of the user input pattern profiles. The present invention processes each user input pattern profile to identify a demographic type. A plurality of biometric behavior models are employed to identify a unique demographic type. Each user input pattern profile is compared against the plurality of biometric behavior models to match each user input pattern profile with one of the biometric behavior models such that each user input pattern profile is correlated with one demographic type. Audience analytics are then based upon the identified demographic types.

A system, method and database development tool are disclosed for automatically generating the complete dependency graph for use in debugging stored code objects in a database, by using a recursive dependency tracking algorithm which takes into consideration the indirect dependencies on triggers as well as the dependencies on implementations of object oriented code objects which are represented as separate objects in the database catalog.

A bandwidth efficient advanced modulation waveform modem using concatenated iterative turbo coding and continuous phase modulation is disclosed. A demodulator in the modem has a turbo decoder and a decision feedback carrier and time tracking algorithm to track a carrier and adjust timing. The decision feedback carrier and time tracking algorithm may use an APP decoder as a decision device to provide symbol decisions at a high error rate and low latency for a coded input data stream. A symbol phase estimator produces a symbol phase error estimate from the symbol decisions. An erasure decision function decides which symbol decisions are correct and which symbol decisions are erasures. A carrier tracking function receives the symbol phase error estimates when the symbol decisions are correct and receives erasure inputs when the symbol decisions are erasures to maintain carrier tracking.

This invention puts forward a method and a system for real time testing and continuous tracking of man-face in a video sequence including the following steps: carrying out a test algorithm to a man-face input video image, then applying a rough and fine two stages of test algorithm to verify the tested face, applying an object following algorithm to follow the verified one and verifying it by verifying the followed regions, which realizes a real time test to a positive and vertical man-face by a test method based on AdaBoost statistic hierarchical sorter and Mean shift and a square pattern property.

The present invention is a system, method, and apparatus for determining the impact of crowding on retail performance based on a measurement for behavior patterns of people in a store area. The present invention captures a plurality of input images of the people by at least a means for capturing images, such as cameras, in the store area. In the captured plurality of input images, each person's shopping path is detected by a video analytics-based tracking algorithm. A subset of the people is identified as a crowd in the store area. In relation to the crowd, the behavior patterns of the target person are measured. After aggregating the measurements for the behavior patterns over a predefined window of time, the present invention can calculate a crowd index and a crowd impact index for the store area based on the measurements. A crowd index shows the level of crowd density in the store area caused by a crowd, including traffic count of the crowd in the store area. A crowd impact index comprises a traffic count of the target people who make trips to the store area and a shopping time index, such as average shopping time changes of the target people, in relation to a crowd in the measured store area.

The invention discloses an interactive remote expert cooperation maintenance system and a method based on an augmented reality technology. An on-site client side carries out video acquisition or image shooting on equipment to be detected and maintained, and transmits to a remote server side; the remote server side receives a video or an image sent by the on-site client side, receives drawing processing on the video or the image carried out by an expert, and sends the drawn target area image to the on-site client side; after receiving the drawn target area image, the on-site client side completes recognition, location and tracking of a target area in the target area image and an on-site video image through an object tracking algorithm; a detector finds out the target area image; a tracker tracks a target; the detection accuracy of the detector is improved continually through P-N Learning; a marked pattern or a text comment, and current equipment state information are overlapped at the position of the target area in the on-site video image, and an augmented reality image video is generated; the augmented reality technology is utilized to synthesize the augmented reality video, and the fault diagnosis and the maintenance of on-site equipment are completed with the cooperation of the expert.

The invention relates to an automatic target tracking method and system by combining multi-characteristic matching and particle filtering. The target tracking system provided by the invention comprises a video acquisition module, a tracking algorithm computation module and an output control module, wherein the video acquisition module finishes the initialization of an acquisition card and the real-time acquisition of images; the tracking algorithm computation module comprises three tracking modes: the particle filtering tracking based on gray template region matching, the particle filtering tracking based on color probability distribution and the particle filtering tracking based on SIFT (scale invariant feature transform) characteristic matching, thereby realizing the target tracking of translation space and affine space; and the output control module utilizes the center of the target position obtained by tracking as a control command which is transmitted to a cloud deck, thereby realizing the motion of a camera along with a target object.

The invention discloses a double video camera interactive intelligent tracking teaching system. The double video camera interactive intelligent tracking teaching system comprises an image collecting module, a video camera control module, a user interface interaction module, a thread interaction module, a teacher tracking module, a student testing module and a student tracking module. During the teaching process, a control main engine obtains images which are photographed by teachers and students in real time through a dual image acquisition card. A tracing algorithm and a detecting algorithm based on an image processing technique are used for calculating the results and choosing appropriate control strategies. Mutual change among signals are reasonably arranged, functions of the live video camera including swaying, pitching, zooming and focusing can be rapidly, flexibly and accurately achieved. Teachers are capable of locating a proper position in the shooting picture, and functions like teachers' automatic tracing and positioning, students' video mapping and offering a close-up view while questioning testing and writing on the blackboard are achieved. In the end, the automatic intelligent tracing teaching system of tracing the external disturbance, no specific behavior limits and shooting without specially-assigned person on guard is achieved.

The invention provides a kernel correlation filtering target tracking method based on feature fusion and Bayesian classification. The method is characterized by comprising steps: firstly, the position and the scale information of an initial frame target are given; then, a standard target appearance model, a standard correlation filter and a standard color Bayesian model are built or updated; based on the target center point of a former frame, a search area is extracted; and a correlation filter of a Gaussian kernel is used for realizing target displacement estimation, the color Bayesian model is used for realizing target scale estimation, and the tracking result of the current frame is obtained further. Through sequentially processing each frame of video image, tracking of a moving target in the video is realized. The target tracking method can effectively solve the problem of precisely positioning the moving target in the video and can realize target scale estimation. In multiple challenging environments, the target can be tracked effectively, certain robustness is realized, and the tracking algorithm precision is improved.

The invention provides a face detecting and tracking method and a device. The method comprises the steps of inputting a face image or a face video, preprocessing the face image or the face video in an illumination mode, detecting a face by usage of an Ada Boost algorithm, confirming an initial position of the face, and tracking the face by the usage of a Mean Shift algorithm. According to the face detecting and tracking method and the device, a self-adaptation local contrast enhancement method is provided to enhance image detail information in the period of image preprocessing, in order to increase robustness under different illumination conditions, face front samples under different illumination are added to training samples and accuracy of the face detection is increased by adoption of the Ada Boost algorithm in the period of face detection, in order to overcome the defect that using color of the Mean Shift algorithm is single, grads features and local binary pattern length between perpendiculars (LBP) vein features are integrated by adoption of the Mean Shift tracking algorithm in the period of face tracking, wherein the LBP vein features further considers using LBP local variance for expressing change of image contrast information, and accuracy of the face detection and the face tracking is improved.

A method and apparatus are disclosed that enable an over-the-horizon-radar (OTHR) system to detect and track multiple target classes simultaneously, where target classes are defined by the speed and acceleration of the tracked objects. The OTHR is tasked in a staring mode, with a bandwidth and waveform repetition frequency that enable detection of Doppler shifts from all target types, with sufficient clutter reduction and range resolution. The backscattered echoes are buffered for each target class and processed independently. The output of an automatic tracking algorithm then preferentially plots target progress on a single digital map for all target classes.

The traffic speed counting and matching method for road traffic includes: fetching position information of mobile terminals from collected traffic data information; when the position information of the mobile terminals is lost, according to the change information of the position information of the mobile terminals, dynamic predicting and obtaining the position and speed information of the mobile terminals by using follow-up arithmetic; matching the position and speed information of the mobile terminals onto a road network to generate road traffic speed information. The road traffic speed counting and matching system is also disclosed. The method and system can provide exact traffic condition information to traffic managers and going out people.

The invention provides face detection and tracking algorithm of the infrared thermal image sequence, which is characterized by carrying out face detection on the infrared thermal images in combination with the face detection algorithm of the visible-light images and carrying out face tracking on the infrared thermal image sequence in combination with the face tracking algorithm of the visible-light image sequence. The algorithm provided by the invention can realize selective target (face) temperature measuring and selective target (face reaching the warning temperature) tracking and temperature measuring. The algorithm provided by the invention avoids the difficulty of whole registration of the infrared thermal images and the visible-light images with different properties. The algorithm is applied to the thermal infrared imagers and can organically combine the infrared thermal image information systems and the visible-light image information systems of the thermal infrared imagers, construct an image information system with certain intelligent information processing capability and expand the application field of the thermal infrared imagers.

A semi-automatic method of tracking color objects in a video image sequence starts by separating the objects on the basis of color and identifying an object of interest to track. A Kalman predictive algotithm in used to predict the position of the centroid of the object of interest through successive frames. From the predicted position the actual centroid is measured and the position and velocity are smoothed using a Kalman filter. Error recovery is provided in the event the centroid falls outside the field of view or falls into an area of a different color, or in the event the tracking algorithm breaks down.

The invention discloses a somatosensory-based natural interaction method for a virtual mine. The method comprises the steps of applying a Kinect to acquire gesture signals, depth information and bone point information of a user; carrying out smoothing filtering on images, depth information and bone information of the gesture signals; dividing gesture images by using a depth histogram, applying an eight neighborhood outline tracking algorithm to find out a gesture outline, and identifying static gestures; planning feature matching identification of dynamic gestures by improving dynamic time according to the bone information; triggering corresponding Win32 instruction information by using a gesture identification result, and transmitting the information to a virtual reality engine, respectively mapping the instruction information to the primary keyboard mouse operation of a virtual mining natural interaction system, so as to realize the somatosensory interaction control of the virtual mine. According to the method provided by the invention, the natural efficiency of man-machine interaction can be improved, and the immersion and natural infection represented by the virtual mine can be improved, and the application of the virtual reality and a somatosensory interaction technology can be effectively popularized in coal mines and other fields.

The invention discloses an unmanned aerial vehicle inspection head control method based on visual servo. The method comprises the steps of: 1, obtaining video information by an imaging device; 2, matching a frame real-time image with a template image, obtaining a pixel deviation and determining a deviation P of the image center; 3, judging whether P is greater than a deviation threshold, if not, indicating a normal situation, and finishing this detection, if so, turning to the next step; 4, determining a rotating direction through P, then rotating the holder for the minimal unit d; 5, obtaining the device image at a current position again; 6, trying a new target position by a tracing algorithm; determining the deviation P1 of the new position and the template image; 7, judging whether P is greater than the threshold according to the linear relationship between holder rotation and the pixel deviation in the image, if not, finishing this detection, and if so, returning to the step 5. Location filming of the target in the unmanned aerial vehicle inspection process is effectively solved, and the inspection efficiency and quality are improved.

The invention provides a full-automatic anti-intrusion intelligent video monitoring alarm system for an unattended villa, which relates to the technical field of intelligent monitoring systems. The anti-intrusion intelligent video monitoring alarm system comprises a video intelligent analysis system, an anomalous event analysis system and an anomalous event alarm system. The video intelligent analysis system particularly refers to automatically detecting an object through a subregion autonomous non-reference threshold algorithm and an object key information secondary extraction algorithm and automatically tracking the object by adopting a movement trend tracking algorithm merging Kalman filtering and particle filtering. By detecting and tracking the movement objective track information, the movement direction, the horizontal and vertical displacement, and the object center position of the object are determined. The anomalous event analysis system particularly refers to analyzing video signals acquired by a villa monitoring camera through a machine vision algorithm, judging anomalous and dubious behaviors of the objects (personnel) around and in a villa through a preset rule, and notifying information on pictures, characters and the like of the anomalous behaviors and events to a proprietor or a security department in time.

The invention relates to a video monitoring system and method for target detection and tracking. The system comprises a video collection device, a target detection device, an information processing device, an information transmission device and an image collection device, wherein the image collection device is composed of a front end image collection device and a video decoding device and is usedfor obtaining a real-time video stream of a target object; the target detection device analyzes the position and the size of the target object in a video image; the information processing device performs real-time processing on related information of the target object within a continuous time to obtain a moving speed, a trajectory and direction information of the target object, and judges the number, the location and the size information of the target object in advance; the information transmission device sends the related information of the target object to the image collection device; and the image collection device controls the target object to always locate in a middle area of the screen and tracks the target object in real time. According to the video monitoring system and method, thetracking of numerous small targets is facilitated according to the deep neural network with multiple features, the recognition rate is improved, and it is beneficial to the good running of the tracking algorithm, and real-time tracking of the target object is achieved.

The invention belongs to the field of intelligent traffic video image monitoring and video image analysis, and in particular relates to a method and a system for automatically identifying an urban traffic accident. The method for automatically identifying the traffic accident comprises the following steps of: acquiring an urban road video image sequence; performing foreground vehicle separation based on a mixed Gaussian background model; performing a multi-target vehicle tracing algorithm based on a Camshift algorithm and a kalman filtering combination; extracting traffic accident determiningparameters such as speed variation, horizontal position variation, vertical position variation, moving direction variation and the like; and proposing a multi-featured weighted fusion automatic accident identification algorithm. Traffic accident information is transmitted to a traffic control center in time by a transmission unit and a display unit, so that the traffic accident can be quickly treated, an effective and flexible road traffic monitoring means with high cost performance is provided for traffic management, and new thought is provided for the development of a high-efficiency intelligent video traffic accident system.

The invention discloses a suspicious target detection tracking and recognition method based on dual-camera cooperation, and belongs to the technical field of video image processing. The method comprises the steps that a panoramic surveillance camera is utilized for collecting a panoramic image, the improved Gaussian mixture modeling method is adopted for carrying out foreground detection, basic parameters of moving targets are extracted, a Kalman filter is utilized for estimating a movement locus of a specific target, the specific target is recognized according to velocity analysis, the dual-camera cooperation strategy is adopted, a feature tracking camera is controlled to carry out feature tracking on the moving targets, a suspicious target is locked, the face of the suspicious target is detected, face recognition is carried out, face data are compared with a database, and an alarm is given if abnormities exist. According to the suspicious target detection tracking and recognition method, the dual-camera cooperation tracking surveillance strategy is adopted, defects of a single surveillance camera on a specific scene are overcome, and the added face recognition function can assist workers in identifying the specific target to a greater degree; in addition, the tracking algorithm adopted in the method is good in real-time performance, target recognition and judgment standards are simple and reliable, and the operation process is fast and accurate.

The invention discloses a method for generating video abstract on the basis of a deep learning technology. The method includes modeling backgrounds of video stream frame by frame and acquiring moving foregrounds to be used as candidate moving objects; tracking the candidate moving objects of each frame by the aid of a multi-object tracking algorithm and updating candidate objects which form movement tracks; training object classifiers by the aid of convolutional neural networks, confirming the candidate objects and determining categories of the objects by the aid of the classifiers after real moving objects are confirmed; fitting all the real moving objects and relevant information on a small quantity of images, forming video snapshots and displaying the video snapshots to users. The method has the advantages that the real objects and noise can be accurately differentiated from one another by the aid of the deep learning technology; the objects do not need to be confirmed frame by frame owing to an accurate multi-object tracking technology, accordingly, the computational complexity can be greatly reduced, an omission factor of the objects and a false alarm rate of the noise can be effectively reduced, the video processing speeds can be increased, and the method can be applied to various complicated scenes.

The invention discloses a tracking algorithm for high maneuvering targets. Under the colored noise condition, a target movement model and an observation model are established; the maneuvering targets are tracked by adopting an interactive multi-mode (IMM) algorithm-based Kalman filter; and a 'current' statistical model which is self-adaptively adjusted by acceleration in the IMM algorithm is combined with CV and CA models. The integral IMM algorithm performance is improved; by using system mode information in the current measurement, a Markov transition probability is calculated on line in real time, so that an accurate posterior estimate is obtained, and the model fusion precision is improved.

The invention discloses a motion control system and a motion control method for a spherical robot with visual feedback. The motion control system comprises a binocular visual system, a gyroscope, a spherical robot body, an embedded-type controller and a wireless communication module. The motion control system is positioned through cooperation between a visual camera and the gyroscope and measures self motion parameter information of the robot in real time, and the self motion parameter information is used as feedback to be input into a controller. After conducting calculations, the controller issues a control order to a motor on the spherical robot so as to achieve tracking of a targeted path. Meanwhile, the controller can also monitor state information of the robot remotely and give an operation order. The motion control method sets up an inner ring control strategy by adopting a state feedback algorithm, sets up an outer ring control strategy by adopting a curvature tracking algorithm and conducts control through combining an inner ring and an outer ring. The left and right image sequence can be collected in real time through the binocular visual system. By means of adoption of a binocular visual range algorithm, postures and changes of position of the spherical robot can be figured out. By mean of the gyroscope, errors caused by an unstable platform are complemented, and obtained results are used as feedback to be brought into a control algorithm.

A method and system is configured to characterize regions of an environment by the likelihoods of transition of a target from each region to another. The likelihoods of transition between regions is preferably used in combination with conventional object-tracking algorithms to determine the likelihood that a newly-appearing object in a scene corresponds to a recently-disappeared target. The likelihoods of transition may be predefined based on the particular environment, or may be determined based on prior appearances and disappearances in the environment, or a combination of both. The likelihoods of transition may also vary as a function of the time of day, day of the week, and other factors that may affect the likelihoods of transitions between regions in the particular surveillance environment.

The invention discloses an in-orbit moving target detecting method with space-based radar and infrared data fusion. The method includes the steps that when an infrared detecting system is used for detection, background suppression is carried out on each frame in an infrared image sequence by adopting a filtering method based on the morphology, self-adaptive threshold segmentation is carried out on images obtained after background suppression, a single frame detection result is extracted, a multi-frame target is determined through a tracking algorithm, and whether the detection result is a real target or not is judged to obtain the detection result of the multi-frame target; during radar detection, pulse doppler (PD) is adopted for processing. After time calibration and space calibration are carried out on the obtained infrared and radar target information, data fusion is carried out on the information by adopting a measurement fusion method, and track prediction is carried out on fused data to obtain the estimated position of the target.