unusualevent

unusualevent

• 1.
  REAL-TIME DETECTOR FORUNUSUAL BEHAVIOR Showcase
• 2.
  Highlights Events usual  non-usual Motion and shape based Statistically relevant  irrelevant Alert generation on unusual event Storing events in database
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  Platform Visualisation: Webbrowser SZTAKI will provide a communication module that will call the module functions provided by the partners. Software platform: C++, OpenCv, IPP Web technologi Hard ware platform: Pc, laptop (x86 like)
• 4.
  Partners ACV BILKENTUPC SZTAKI Tracking, pedestrian detection Multimodal human actions, HMM 2D Body actions, motion fields Unusual event detection, annotation process, statistical analysis, shadow removing ( f ormerly A RC ) BILKENT
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  Distribution of workMoving Cam. Static Cam. mosaicing Foreground Detect. shilouettes HMM class. Body model Motion features Periodicity Pedestrian detection Tracking sound classification Unusual event Region alert Sztaki ACV BILKENT UPC
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  Contribution of ACVNon-parametric clustering of moving objects in difference images Occlusion handling for interacting targets Kernel-based tracking using motion features for multiple targets Video data set and evaluation of the motion detection and tracking performance (Benchmark competition)
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  Details on thealgorithmic modules Human detection by clustering and model-based verification VIDEO Kernel-based human tracking using motion information VIDEO Occlusion handling VIDEO Tracking evaluation (comparison to manual ground truth)
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  Evaluation video datasets(street scenarios) Sequence Street_01.avi: 720x576 pixels, 8628 frames (tracking ground truth available for 1040 frames) Sequence Street_02.avi: 720x576 pixels, 763 frames (tracking ground truth available for 763 frames)
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  Contribution of Bilkent Motion and silhoutte based person detector detect motion and moving blocks and observe periodicity in bounding boxes of moving blocks in video. use silhouttes to classify moving objects in video combine the results of periodicity and silhoutte based detectorIn this way, Determine the number of people in the scene. HMM classification (fight, fall or simply walk) Record the sounds and classify the sounds to (car sounds, walking person, and loud screems) Combine the results of 3 and 4 to reach a final decision. BILKENT
• 10.
  Human Recognition inVideo Utilizes objects’ silhouettes for different poses Silhouettes are extracted using contour tracing Compare silhouette signature functions using wavelet energy signatures BILKENT
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  Observation: Walkingand falling person Falling Person Detection using Motion Clues ( visual ) T1 T2 BILKENT
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  Contribution of UPC Foreground detection and automatic features extraction motion history descriptors simple body model Apply the integrated system to different environments crowded scenes in automatic stairs
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  Motion Analysis MotionHistory and Motion Energy descriptors introduced by Bobick et al. in 2D and Canton et al. in 3D allows robust motion analysis MEV MHV
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  Model Based AnalysisAnalyzing input data by means of a Human Body Model, allows retrieving information about limbs positions
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  Silhouette analysis fordetection of body extremities Scene capture User segmentation CoG computation Creation of the geodesic distance map Contour tracking Creation of the distance/silhouette border position function H-maxima operation on the function Local maxima extraction Morphological skeleton computation and crucial point labeling Pixel position Geodesic Distance
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  Contribution of SZTAKIForeground detection View region surveillance Alert event generation Event History Search & display
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  Contribution of SZTAKIForeground detection in moving camera
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  Contribution of SZTAKIMosaicing
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  Contribution of SZTAKIUsual – non usual motion Pixel-wise motion estimation black: right, white: left Motion statistics Input Actual motion masked with usual motion
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  Contribution of SZTAKISG based unusuality detector on motion fields motion tracks Software Environment Interface module to user dll /lib/module Separates and bridge modules Server Serves image/video streams Transcodes images Forward requests to modules DB server Metadata store & search Webserver Generate html pages with links to Server (later) Client dynamic web Javascript/flash based graphics display Mozilla native mjpeg stream + SVG
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  Web Page DBmetadata tcp/ip SERVER Web server Matlab C++ DLL/LIB Comm. Interface json tcp/ip tcp/ip mjpg json Html User modules Contribution of SZTAKI - architecture Data Source Controller Comm. Interface Comm. Interface Comm. Interface Module Register Streams Internet