Search results for "Object Detection"

showing 10 items of 64 documents

Real-Time Hand Pose Recognition Based on a Neural Network Using Microsoft Kinect

2013

The Microsoft Kinect sensor is largely used to detect and recognize body gestures and layout with enough reliability, accuracy and precision in a quite simple way. However, the pretty low resolution of the optical sensors does not allow the device to detect gestures of body parts, such as the fingers of a hand, with the same straightforwardness. Given the clear application of this technology to the field of the user interaction within immersive multimedia environments, there is the actual need to have a reliable and effective method to detect the pose of some body parts. In this paper we propose a method based on a neural network to detect in real time the hand pose, to recognize whether it…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniArtificial neural networkgesture recognitionbusiness.industryComputer scienceMicrosoft Kinect.gesture-based interactionVirtual realityObject detectionhuman-computer interactionFeature (computer vision)Gesture recognitionComputer visionArtificial intelligenceNoise (video)businessPoseGesture2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications
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Real-time estimation of geometrical transformation between views in distributed smart-cameras systems

2008

In this paper, we present a method to automatically estimate the geometric relations among the different views of cameras with partially overlapping fields of view in a wireless video-surveillance system. The method uses the locations of the detected moving objects visible at the same time in two or more views. The correspondences among objects are found by comparing their appearance models based on dominant colour descriptors while the geometric transformation are computed iteratively and may be used to solve the consistent labelling problem. As a significant part of the processing is performed on the smart cameras, the method has been conceived by taking into account the limited resources…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniComputer sciencebusiness.industryGeometric transformationMotion detectiondistributed video surveillanceObject detectionData modelingTransformation (function)Computer visionSmart cameraArtificial intelligenceImage sensorbusinessHomography (computer vision)2008 Second ACM/IEEE International Conference on Distributed Smart Cameras
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Texture classification for content-based image retrieval

2002

An original approach to texture-based classification of regions, for image indexing and retrieval, is presented. The system addresses automatic macro-textured ROI detection, and classification: we focus our attention on those objects that can be characterized by a texture as a whole, like trees, flowers, walls, clouds, and so on. The proposed architecture is based on the computation of the /spl lambda/ vector from each selected region, and classification of this feature by means of a pool of suitably trained support vector machines (SVM). This approach is an extension of the one previously developed by some of the authors to classify image regions on the basis of the geometrical shape of th…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniContextual image classificationComputer sciencebusiness.industryFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionImage segmentationContent-based image retrievalCBIR texture analysisObject detectionImage textureFeature (computer vision)Computer visionArtificial intelligencebusinessImage retrievalProceedings 11th International Conference on Image Analysis and Processing
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A MOBILE ROBOT FOR TRANSPORT APPLICATIONS IN HOSPITAL DOMAIN WITH SAFE HUMAN DETECTION ALGORITHM

2009

We have been developing a MKR (Muratec Keio Robot), an autonomous omni-directional mobile transfer robot system for hospital applications. This robot has a wagon truck to transfer luggage, important specimens and other materials. This study proposes a safe obstacle collision avoidance technique that includes a human detection algorithm for omni directional mobile robots that realizes a safe movement technology. The robot can distinguish people from others obstacles with human detection algorithm. The robot evades to people more safely by considering its relative position and velocity with respect to them. Some experiments in a hospital were carried out to verify the performance of the human…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniEngineeringbusiness.industryMobile robotObject detectionMobile robot navigationRobot controlDomain (software engineering)Mobile Robot Human-Robot Interaction Safe NavigationObstacleRobotbusinessAlgorithmSimulationCollision avoidance
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Real-Time Object Detection in Embedded Video Surveillance Systems

2008

In this paper we report a new method to detect both moving objects and new stationary objects in video sequences. On the basis of temporal consideration we classify pixels into three classes: background, midground and foreground to distinguish between long-term, medium-term and short-term changes. The algorithm has been implemented on a hardware platform with limited resources and it could be used in a wider system like a wireless sensor networks. Particular care has been put in realizing the algorithm so that the limited available resources are used in an efficient way. Experiments have been conducted on publicly available datasets and performance measures are reported.

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniPixelBasis (linear algebra)business.industryComputer scienceReal-time computingVideo sequencevideo surveillance embedded systemsObject detectionTerm (time)Statistical classificationComputer visionArtificial intelligencebusinessWireless sensor networkLimited resources2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
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Midground Object Detection in Real World Video Scenes,

2007

Traditional video scene analysis depends on accurate background modeling to identify salient foreground objects. However, in many important surveillance applications, saliency is defined by the appearance of a new non-ephemeral object that is between the foreground and background. This midground realm is defined by a temporal window following the object's appearance; but it also depends on adaptive background modeling to allow detection with scene variations (e.g., occlusion, small illumination changes). The human visual system is ill-suited for midground detection. For example, when surveying a busy airline terminal, it is difficult (but important) to detect an unattended bag which appears…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScene statisticsObject (computer science)Object detectionObject-class detectionComputational efficiencyComputer networksSalientVideo trackingHuman visual system modelComputer visionViola–Jones object detection frameworkArtificial intelligencebusiness
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Views selection for SIFT based object modeling and recognition

2016

In this paper we focus on automatically learning object models in the framework of keypoint based object recognition. The proposed method uses a collection of views of the objects to build the model. For each object the collection is composed of N×M views obtained rotating the object around its vertical and horizontal axis. As keypoint based object recognition using a complete set of views is computationally expensive, we focused on the definition of a selection method that creates, for each object, a subset of the initial views that visually summarize the characteristics of the object and should be suited for recognition. We select the views by determining maxima and minima of a function, …

Similarity (geometry)Computer science3D single-object recognitionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONLearning objectScale-invariant feature transform02 engineering and technologySIFT0202 electrical engineering electronic engineering information engineeringMedia TechnologyComputer vision060201 languages & linguisticsObject RecognitionSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryFeature matchingCognitive neuroscience of visual object recognitionPattern recognition06 humanities and the artsObject (computer science)Object Modeling0602 languages and literatureSignal ProcessingObject model020201 artificial intelligence & image processingViola–Jones object detection frameworkArtificial intelligencebusiness
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An Embedded Real-Time Lane-Keeper for Automatic Vehicle Driving

2008

Automatic vehicle driving involves several issues, such as the capability to follow the road and keep the right lane, to maintain the distance between vehicles, to regulate vehiclepsilas speed, to find the shortest route to a destination. In this paper a real-time automatic lane-keeper is proposed. The main features of the system are the lane markers location process as well as the computation of the vehiclepsilas steering lock. The above techniques require high elaboration speed to execute, check and complete an operation before a prearranged time. Clearly if system processing exceeds the deadline, the whole operation became meaningless or, in the meantime, the vehicle can reach a critical…

VirtexAutomatic controlComputer scienceComputationReal-time computingImage segmentationField-programmable gate arrayVehicle drivingCritical conditionObject detection2008 International Conference on Complex, Intelligent and Software Intensive Systems
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Detection and matching of curvilinear structures

2011

We propose an approach to curvilinear and wiry object detection and matching based on a new curvilinear region detector (CRD) and a shape context-like descriptor (COH). Standard methods for local patch detection and description are not directly applicable to wiry objects and curvilinear structures, such as roads, railroads and rivers in satellite and aerial images, vessels and veins in medical images, cables, poles and fences in urban scenes, stems and tree branches in natural images, since they assume the object is compact, i.e. that most elliptical patches around features cover only the object. However, wiry objects often have no flat parts and most neighborhoods include both foreground a…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingMatching (graph theory)Computer science[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing02 engineering and technology01 natural sciences010309 optics[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingArtificial Intelligence0103 physical sciences0202 electrical engineering electronic engineering information engineeringSegmentationComputer visionComputingMilieux_MISCELLANEOUSCurvilinear coordinatesbusiness.industryObject (computer science)Object detectionTree (data structure)Signal ProcessingPattern recognition (psychology)020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligenceScale (map)business[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSoftware
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Improving Video Object Detection by Seq-Bbox Matching

2019

International audience

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Matching (statistics)business.industryComputer science02 engineering and technology010501 environmental sciences01 natural sciencesObject detection[INFO.INFO-ES] Computer Science [cs]/Embedded Systems[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer vision[INFO.INFO-ES]Computer Science [cs]/Embedded SystemsArtificial intelligencebusinessComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciences
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