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146 results about "Object-class detection" patented technology
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Object class detection is a computer technology that deals with detecting objects of a certain class in digital images and videos. Well-researched domains of object class detection include face detection and pedestrian detection. Object class detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Object detection and detection confidence suitable for autonomous driving
ActiveUS20190258878A1Accurately indicatedFalse detectionScene recognitionMachine learningGround truthObject-class detection
In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.
Owner:NVIDIA CORP
Object detection and tracking method of fusing laser point clouds and images
ActiveCN108509918AImprove detection efficiencyHigh precisionScene recognitionBiometric pattern recognitionFault tolerancePoint cloud
The invention discloses an object detection and tracking method of fusing laser point clouds and images. The method comprises the steps of: S1, respectively collecting laser point cloud data and imagedata of an object, carrying out first object detection according to the collected point cloud data to obtain a first object detection result, and carrying out second object detection according to thecollected image data to obtain a second object detection result; S2, carrying out fusion judgment on the first object detection result and the second object detection result on the basis of Bayesiandecision to obtain final object detection result output; and S3, carrying out object tracking according to a final object detection result. The method has the advantages of a simple and highly-efficient realization method, high object detection and tracking accuracy, high environment adaptability and fault tolerance, stability, reliability and the like.
Owner:NAT UNIV OF DEFENSE TECH
Robust static and moving object detection system via attentional mechanisms
Described, is a system for object detection via multi-scale attentional mechanisms. The system receives a multi-band image as input. Anti-aliasing and downsampling processes are performed to reduce the size of the multi-band image. Targeted contrast enhancement is performed on the multi-band image to enhance a target color of interest. A response map for each target color of interest is generated, and each response map is independently processed to generate a saliency map. The saliency map is converted into a set of detections representing potential objects of interest, wherein each detection is associated with parameters, such as position parameters, size parameters, an orientation parameter, and a score parameter. A post-processing step is applied to filter out false alarm detections in the set of detections, resulting in a final set of detections. Finally, the final set of detections and their associated parameters representing objects of interest is output.
Owner:HRL LAB
Apparatus and method for object detection and tracking and roadway awareness using stereo cameras
ActiveUS8108119B2Performance-enhanced approachComponent is expensiveImage analysisDigital data processing detailsControl systemStereo cameras
The present invention provides a collision avoidance apparatus and method employing stereo vision applications for adaptive vehicular control. The stereo vision applications are comprised of a road detection function and a vehicle detection and tracking function. The road detection function makes use of three-dimensional point data, computed from stereo image data, to locate the road surface ahead of a host vehicle. Information gathered by the road detection function is used to guide the vehicle detection and tracking function, which provides lead motion data to a vehicular control system of the collision avoidance apparatus. Similar to the road detection function, stereo image data is used by the vehicle detection and tracking function to determine the depth of image scene features, thereby providing a robust means for identifying potential lead vehicles in a headway direction of the host vehicle.
Owner:ZAMA INNOVATIONS LLC
Foreground object detection in a video surveillance system
ActiveUS20110052003A1Improve tracking performanceImage enhancementImage analysisVideo monitoringMotion field
Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the detected foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.
Owner:MOTOROLA SOLUTIONS INC
Three-dimensional (3D) object detection and multi-agent behavior recognition using 3D motion data
The present invention relates to an object detection and behavior recognition system using three-dimensional motion data. The system receives three-dimensional (3D) motion data of a scene from at least one sensor, such as a LIDAR sensor. An object is identified in the 3D motion data. Thereafter, an object track is extracted, the object track being indicative of object motion in the scene over time. Through Dynamic Time Warping (DTW) or other comparison techniques, the object track is compared to a database to identify the behavior of the object based on its object track.
Owner:HRL LAB
Small object semantic segmentation method combined with object detection
ActiveCN109145713AImprove Segmentation AccuracyExcellent small object segmentation performanceCharacter and pattern recognitionNeural architecturesData setTest phase
The invention discloses a small target semantic segmentation method combined with target detection. Attention semantic segmentation network, training the network to get the whole semantic segmentationmodel; making small target detection dataset and small target semantic segmentation dataset; training the small target detection network based on YOLOv2 through the small target detection data set; asmall target semantic segmentation network is designed and trained by using the small target semantic segmentation data set to obtain the small target semantic segmentation model. In the testing phase, the test image is used as the input of the whole semantic segmentation model and the small target detection network, and the segmentation result and the small target boundary box of the whole imageare obtained, which is modified by the small target semantic segmentation model. The invention can greatly reduce the segmentation difficulty of the small target, thereby effectively improving the segmentation performance of the small target.
Owner:NANJING NORMAL UNIVERSITY
Method, device, and computer-readable medium of object detection
ActiveUS8401232B2Good flexibilityImprove detection accuracyUltrasonic/sonic/infrasonic diagnosticsImage enhancementObject basedObject-class detection
Disclosed are an object detection method and an object detection device. The object detection method comprises a step of obtaining plural detection results of a current frame according to plural object detection methods; a step of setting initial probabilities of the plural detection results of the current frame; a step of calculating a movement frequency distribution diagram representing movement frequencies of respective pixels in the current frame; a step of obtaining detection results of a previous frame; a step of updating the probabilities of the plural detection results of the current frame; and a step of determining a final list of detected objects based on the updated probabilities of the plural detection results of the current frame.
Owner:RICOH KK
Method and system to perform optical moving object detection and tracking over a wide area
ActiveUS20100157055A1Robust and reliable and dataRobust and reliable positionImage enhancementImage analysisObject-class detectionObject detection
A method for moving object detection, comprising generating a time series of multi-exposures of scenes, each multi-exposure of a scene comprising a sequence of at least two at least partially overlapping images of that scene captured in rapid succession, wherein the time series of multi-exposures periodically revisits substantially the same scenes, detecting moving objects within each multi-exposure by comparing its sequence of overlapping images, and tracking objects by comparing moving objects detected within multi-exposures of substantially the same scenes.
Owner:STATE OF ISRAEL MINIST OF AGRI & RURAL DEV AGRI RES ORG (A R O) (VOLCANI CENT)
Foreground object detection in a video surveillance system
ActiveUS8218819B2Improve tracking performanceImage enhancementImage analysisVideo monitoringMotion field
Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the detected foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.
Owner:MOTOROLA SOLUTIONS INC
Surveillance video abstraction extraction method based on moving object detection
InactiveCN104331905AImprove processing efficiencyImage analysisCharacter and pattern recognitionObject-class detectionBackground image
The invention discloses a surveillance video abstraction extraction method based on moving object detection. The surveillance video abstraction extraction method comprises the steps of firstly determining the parameters of a ViBe model, initializing the model by use of a section of video frames and treating new video frames by use of the obtained ViBe background model so as to generate binary background images, secondly, performing morphological operation on each obtained binary image and obtaining the position information of a moving object in the frame of the binary image, thirdly, tracking each moving object and storing the motion sequence of each object, fourthly segmenting the moving objects to obtain fine profiles of the objects, and finally, jointing the profile sequences of the motion objects to the background frames by use of a reasonable method. A simple and effective video abstraction extraction method is realized by use of the ViBe model for various surveillance videos, and the processing efficiency is improved on the basis of remaining the motion information of the key moving objects.
Owner:ZHEJIANG UNIV
Multi-stage method for object detection using cognitive swarms and system for automated response to detected objects
A multi-stage method of visual object detection is disclosed. The method was originally designed to detect humans in specific poses, but is applicable to generic detection of any object. A first stage comprises acts of searching for members of a predetermined general-class of objects (such as humans) in an image using a cognitive swarm, detecting members of the general-class of objects in the image, and selecting regions of the image containing detected members of the general-class of objects. A second stage comprises acts of searching for members of a predetermined specific-class of objects (such as humans in a certain pose) within the selected regions of the image using a cognitive swarm, detecting members of the specific-class of objects within the selected regions of the image, and outputting the locations of detected objects to an operator display and optionally to an automatic response system.
Owner:HRL LAB
Image processing apparatus and image processing method
ActiveUS20110096922A1Raise the possibilityClosed circuit television systemsSecret communicationImaging processingObject-class detection
An object detection unit of a network camera detects an object including a human figure from image data. An event detection unit analyzes the image data based on a result of the object detection by the object detection unit. Based on a result of the image data analysis, an event detection unit determines to make identifiable to a client a human figure that should be monitored by the client and to apply masking to a human figure that should not be monitored by the client to make it unidentifiable to the client.
Owner:CANON KK
Surround view camera system for object detection and tracking
InactiveUS20170297488A1Television system detailsImage enhancementObject detectionObject-class detection
A method to equip a vehicle to perform object detection and tracking and a surround view camera system to perform the object detection and tracking involve two or more cameras arranged respectively at two or more locations of the vehicle. The cameras capture images within a field of view of the two or more cameras. A processing system obtains the images from the two or more cameras and performs image processing to detect and track objects in the field of view of the two or more cameras.
Owner:GM GLOBAL TECH OPERATIONS LLC
Object detection and identification method, device, electronic equipment and storage medium
InactiveCN108872991AImprove reliabilityImprove stabilityElectromagnetic wave reradiationRadio wave reradiation/reflectionLaser dataObject-class detection
The invention provides an object detection and identification method, device, electronic equipment and a storage medium; the object detection and identification method based on multi-sensor fusion comprises the following steps: obtaining visual observation data of one or more objects according to image data of an image acquisition device; clustering laser data of a laser radar sensor, and obtaining position information of one or more objects to serve as the laser observation data; obtaining millimeter wave observation data of one or more objects according to millimeter wave data of a millimeter wave radar sensor; fusing and filtering the visual observation data, the laser observation data and the millimeter wave observation data of the same object according to time sequence so as to obtainthe fused observation data, and detecting and identifying the object according to the fusion data. The method and device can improve the object detection and identification reliability, stability andrecognition rate.
Owner:SHANGHAI WESTWELL INFORMATION & TECH CO LTD
Methods and systems for performing sleeping object detection and tracking in video analytics
Owner:QUALCOMM INC
Object detection and image cropping using a multi-detector approach
Computer-implemented methods for detecting objects within digital image data based on color transitions include: receiving or capturing a digital image depicting an object; sampling color information from a first plurality of pixels of the digital image; optionally sampling color information from a second plurality of pixels of the digital image; generating or receiving a representative background color profile based on the color information sampled from the first plurality of pixels; generating or receiving a representative foreground color profile based on the color information sampled from the second plurality of pixels and / or the first plurality of pixels; assigning each pixel a label; binarizing the digital image based on the labels; detecting contour(s) within the binarized digital image; and defining edge(s) of the object based on the detected contour(s). Corresponding systems and computer program products configured to perform the inventive methods are also described.
Owner:KOFAX
Moving object detection and classification image analysis methods and systems
A method for moving objection detection in an image analysis system is provided. The method includes analyzing consecutive video frames from a single camera to extract box properties and exclude objects that are not of interest based upon the box properties. Motion and structure data are obtained for boxes not excluded. The motion and structure data are sent to a trained classifier. Moving object boxes are determined by the trained classifier. The moving object box identifications are provided to a vehicle system. The data sent to the classifier can consist of the motion and structure data, and no deep learning methods are applied to the video frame data. Driver assistance vehicle systems and autonomous driving systems are also provided based upon the moving object box detection.
Owner:RGT UNIV OF CALIFORNIA +1
Method for assisting object detection through semantic segmentation
InactiveCN109784386AEnhanced detection featuresDetection helpsCharacter and pattern recognitionNeural learning methodsComputer moduleObject-class detection
The invention relates to a method for assisting object detection through semantic segmentation. The method comprises the following steps: selecting a trunk network, and setting parameters: semantic segmentation and object detection share the trunk network; designing a spatial information feedback module: fusing the output characteristics of the two modules Block 4' and Block 5' after the semanticsegmentation branch with the output characteristics of the object detection branches Block 4 and Block 5, and feeding the output characteristics of the semantic segmentation branch back to the objectdetection branch to enhance the characteristics of the object detection; design of global attention mechanism module; and designing a semantic segmentation auxiliary object detection structure based on the selected backbone network and the designed spatial information feedback module and global attention mechanism module.
Owner:TIANJIN UNIV
Object detection and image cropping using a multi-detector approach
Computerized techniques for real-time object detection from video data include: defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, each detector being independently configured to detect objects according to a unique set of analysis parameters; receiving a plurality of frames of digital video data, the digital video data depicting an object; analyzing the plurality of frames using the plurality of detectors and in accordance with the analysis profile, wherein analyzing the plurality of frames produces an analysis result for each of the plurality of detectors; determining a confidence score for each of the analysis results; and updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the plurality of detectors based on the confidence scores. Corresponding systems and computer program products are also disclosed.
Owner:KOFAX
System for object detection and recognition in videos using stabilization
ActiveUS8885887B1Reduce and eliminate apparent image motionImage enhancementImage analysisFeature vectorObject Class
Described is a system for stabilizing, detecting, and recognizing objects in video captured from a mobile platform. The system first receives a video (with a plurality of image frames) captured from a mobile platform. The video is stabilized by registering the image frames to a global coordinate system to generate stabilized image frames. A bio-inspired attention algorithm is applied to the stabilized image frames to produce a set of locations in the stabilized image frames that are salient points representative of an object of interest. An image chip is generated that surrounds each salient point. High-dimensional feature vectors are extracted from the image chip. The feature vectors are then classified as an object class. Thus, through classifying the feature vectors, an object of interest can be identified in the video as captured from the mobile platform.
Owner:HRL LAB
Moving object detection method based on Gaussian mixture model and superpixel segmentation
InactiveCN105528794AEliminate distractionsThe detection method is accurateImage enhancementImage analysisObject-class detectionBackground image
The invention discloses a moving object detection method based on a Gaussian mixture model and superpixel segmentation. The task of moving object detection is extracting objects of interest as many as possible from a complex scene and presenting the results in the form of binary images. The method is characterized by, to begin with, carrying out background modeling by utilizing the Gaussian mixture model and obtaining a background image of the current frame; then, carrying out superpixel segmentation on the current frame through an SLIC(simple linear iterative clustering) algorithm; and finally, carrying out LTP (local ternary pattern) texture feature extraction on the segmented images and background images, and then, carrying out comparison to obtain a moving object. The beneficial effects of the method are that the method can detect the moving object in real time according to the video images captured by cameras; and the detection method is efficient and accurate.
Owner:SHANGHAI INST OF TECH
Access point stream and video surveillance stream based object location detection and activity analysis
InactiveUS20160335484A1Character and pattern recognitionClosed circuit television systemsVideo monitoringLocation detection
Methods and systems for co-relating location and identity data available from Access Points (APs) and video surveillance systems are provided. According to one embodiment, data, including a unique identifier of an object and information regarding a first geo-position of the object, is received from an AP of a wireless network of a venue. A video feed captured by a camera system monitoring a portion of the venue and / or information regarding a second geo-position corresponding to the object are also received. The first and second geo-positions are then mapped to a common coordinate system. Based on the unique identifier, information regarding the object as reported by the AP and the camera system or derived therefrom are correlated. Finally, behavioral attributes of the correlated object are assessed based on one or a combination of actions of the object, the first and second geo-positions and the common coordinate system.
Owner:FORTINET
Method, device, and computer-readable medium of object detection
ActiveUS20110274315A1Improve detection accuracyReduce detection error rateUltrasonic/sonic/infrasonic diagnosticsTelevision system detailsObject basedObject-class detection
Disclosed are an object detection method and an object detection device. The object detection method comprises a step of obtaining plural detection results of a current frame according to plural object detection methods; a step of setting initial probabilities of the plural detection results of the current frame; a step of calculating a movement frequency distribution diagram representing movement frequencies of respective pixels in the current frame; a step of obtaining detection results of a previous frame; a step of updating the probabilities of the plural detection results of the current frame; and a step of determining a final list of detected objects based on the updated probabilities of the plural detection results of the current frame.
Owner:RICOH KK
Image retrieval method based on object detection
ActiveCN107256262AReduce distractionsImprove accuracyCharacter and pattern recognitionSpecial data processing applicationsSemantic gapImaging Feature
The invention discloses an image retrieval method based on object detection. The method is used for solving the problem that multiple objects in an image are not retrieved respectively during image retrieval. According to the implementation process of the method, object detection is performed on an image in an image database, and one or more objects in the image are detected; SIFT features and MSER features of the detected objects are extracted and combined to generate feature bundles; a K mean value and a k-d tree are adopted to make the feature bundles into visual words; visual word indexes of the objects in the image database are established through reverse indexing, and an image feature library is generated; and an object detection method is used to make objects in a query image into visual words, similarity compassion is performed on the visual words of the query image and the visual words of the image feature library, and the image with the highest score is output to serve as an image retrieval result. Through the method, the objects in the image can be retrieved respectively, background interference and image semantic gaps are reduced, and accuracy, retrieval speed and efficiency are improved; and the method is used for image retrieval on a specific object in the image, including a person.
Owner:XIDIAN UNIV
Object detection and motion evaluation method for identifying parking space
The present invention relates to a method for recognizing a transverse parking space 32 and auxilarily parking in the transverse parking space 23. A parking assistance system provides a method of identifying a transverse parking space 32. The method also supports parking in the parking space 32. For this purpose, a travel motion of a motor vehicle 10 is evaluated by means of the following method steps: The travel motion 38 is evaluated for straight travel and in particular for portions 44 during which the yaw angle of the motor vehicle 10 remains constant. At least one edge 70, 72 or one corner E1, E2 of a first object 34 delimiting the transverse parking space 32 is detected, and an edge 70, 72 or a corner E1, E2 of a second object 36 delimiting the transverse parking space 32 is detected.
Owner:ROBERT BOSCH GMBH
Moving workpiece recognition method based on spatiotemporal contexts and fully convolutional network
InactiveCN107451601AOvercome the disadvantages of manually assigning the initial position of the targetRealize automatic acquisitionCharacter and pattern recognitionNeural architecturesContext modelVideo sequence
The invention relates to a moving workpiece recognition method based on spatiotemporal contexts and a fully convolutional network, and belongs to the fields of digital image processing and object detection and recognition. According to the method, an object image database is utilized to train the fully convolutional neural network to obtain a classifier of a to-be-classified object; then a background difference method and a morphological method of digital image processing are utilized to obtain an initial position of the object in a first frame of a video sequence, an object tracking method of spatiotemporal context models is utilized to track the to-be-tracked object according to the initial position, and object tracking precision is verified through a precision graph; and finally, the trained classifier is utilized to carry out classification recognition on a tracking result, semantic-level segmentation is realized, and thus an object category is obtained. According to the method, the initial position of the moving object can be effectively and automatically acquired by using the background difference method and the morphological method of digital image processing, tracking and recognition for the moving workpiece on a conveyor belt can be realized, and an automation degree and an intelligence degree of an industrial robot are increased.
Owner:KUNMING UNIV OF SCI & TECH
Method of object consistency detection based on end-to-end deep-learning
InactiveCN107944443ALearning rate reductionCharacter and pattern recognitionNeural architecturesImaging FeatureObject-class detection
The invention provides a method of object consistency detection based on end-to-end deep-learning, and aims to simultaneously find a position, a category and consistency of an object in an image. A region-of-interest alignment layer (RoIAlign) is adopted to correctly calculate features of regions of interest (RoIs) from an image feature graph, a convolution layer sequence is utilized to carry outup-sampling on an RoI feature graph to a high-resolution convolution layer to obtain a consistency graph, and a robustness strategy is adopted to adjust a training model to monitor consistency thereof. Object detection is used for object positioning. Consistency detection allocates each pixel in the object to a consistency label thereof, uses multitask loss to carry out training of bounding-box classification, positions and consistency mapping, and finally carries out training and reasoning to obtain consistency labels. According to the method, end-to-end deep-learning is adopted, a multitaskloss function is used to jointly optimize object detection and consistency detection without the need for additional information, complexity in training and testing processes is reduced, and accuracyof detection is effectively improved. The method is suitable for use in application of real-time robots.
Owner:SHENZHEN WEITESHI TECH
Line removal and object detection in an image
In one aspect, lines in image data of an event are automatically found and repaired. For example, the event may be a sporting event which is played on a field, and the line segment is a field line on the field which may be obscured by a player, game ball or other object. The line segment is automatically detected in a mask image, and a portion of the line segment which is occluded by the object is automatically determined, and the object is automatically removed. The line segment can also be repaired. Optionally, a virtual viewpoint of the event is provided from the image, with the line repaired and the object removed. In another aspect, an object in an image of an event is automatically located by detecting blobs in the image which meet at least one specified criterion, such as size, aspect ratio, density or color profile.
Owner:SPORTSMEDIA TECH CORP
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