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Moving Object Detection with Background Model based on spatio -Temporal Texture

Moving Object Detection with Background Model based on spatio -Temporal Texture. Ryo Yumiba , Masanori Miyoshi,Hirononbu Fujiyoshi WACV 2011. Outline. Introduction ST-patch features Background subtraction Generation of background model Moving object detection Update background model

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Moving Object Detection with Background Model based on spatio -Temporal Texture

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  1. Moving Object Detection with Background Model based on spatio-Temporal Texture Ryo Yumiba, Masanori Miyoshi,HirononbuFujiyoshi WACV 2011

  2. Outline • Introduction • ST-patch features • Background subtraction • Generation of background model • Moving object detection • Update background model • Experimental result

  3. Introduction • Background subtraction is a common method for detecting moving object. • Advantage: not requiring previous knowledge of moving object • Problem: cannot discriminate moving objects from background when these background change significantly • Two approaches to generating BG model covering changes: • Pixel-wise background model • Patch-wise background model • Proposed method cover global changes by using appearance information and it cover local changes by using motion information

  4. ST-PatchFeatures • SP-patch have been used for several applications. • Calculated as statistical values of pixel grayscale gradients within a small patch. • Let be a spatio-temporal gadients appearance information motion information [7]

  5. ST-PatchFeatures • Patch size: 15*15*5(frame) • Appearance components differ between tree and road without regard to motion • Motion components increase according to temporal change • Motion components differ from transitions of sunlight and waving of tree

  6. Generation of Background Model • Use Gaussian mixture distribution of ST-patch to generate background model. • Parameters are calculated previously from examples of background video using EM algorithm • Background changes generally differ according to location  calculate Parameters at each block

  7. Step1: Extract ST-patch at each block of each frame • Step2: Compare with background model • Step3:  moving object • Step4: number of detected block >  moving object candidate flag is set ON • Step5: flag in step4 stays ON more than  active alarm against moving object Detection of moving object

  8. Update of Background Model • It is difficult to generate a background model that wholly covers changes in background in advance  update background model during moving object detection Number of normalized distribution <  add new one Means of distributions are close Weight is less than

  9. Experimental Results • Compare with the method use only appearance features within a patch in image. • Parameter setting • Input : 320×240, 30fps • Patch size : 15*15*5

  10. Experimental Result-- Outdoor Scene • Waving tree, sunlight • Use 1179 frames without pedestrians to generate background model • Regard 1359 frames as frames with moving objects

  11. Experimental Results-- Outdoor Scene 289 frames # FN ↓ (∴background updating)

  12. Experimental Results-- ceiling light scene

  13. Experimental ResultsOutdoor Scene

  14. ExperimentalResults

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