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E VENT D ETECTION U SING AN A TTENTION -B ASED T RACKER 22 Outubro 2007. Universidade Federal do Paraná. Gerald Dalley, Xiaogang Wang, and W. Eric L. Grimson
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Glasgow Airport 4 cameras 9 video clips 1 for training 8 for testing Dataset Description 2
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Loitering Staying in the scene for > 1 minute Theft Taking someone else’s baggage Left luggage Leaving the scene without your luggage Evaluation Correct event times Correct 3D locations Without false detections Challenge Problem 3
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Basic tracking first Detect interesting tracks Improve interesting tracks to detect events Our Approach 4
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Processing Pipeline 5 Tracking & Event Detection Input Data Background Subtraction Background Modeling Background Modeling Illumination Normalization Illumination Normalization
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Illumination Changes 6 BACKGROUND 126 999 2999 S08
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Illumination Changes by Clip 7
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Background Modeling 8 Tracking & Event Detection Input Data Background Subtraction Robust Gaussian Fit Ground-Plane Registration Background Model Search Normalized BACKGROUND Normalized S01-S08 Background Modeling Background Modeling Background Models Illumination Normalization
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Fundamental assumption foreground is rare at every pixel Reality for PETS 2007… background is rare for the pixels we care about most Some pixels: foreground as much as 90% of the time Breaking Adaptive Background Subtraction 9 0 0 2000 3000 4000
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Robust Gaussian Fit (per pixel) 10 BACKGROUND clip Foreground is rare everywhere Fit a Gaussian Refit to inliers
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Need for Model Adaptation 11 Another clip (S02) BACKGROUND’s model: Suboptimal fit BACKGROUND’s Gaussian model Background samples Foreground samples BACKGROUND’s Gaussian model Background samples Foreground samples
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Model Adaptation 12 Until convergence Find inliers Shift Gaussian center
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Improvement 13 Adaptation Is robust Improves FG/BG classification rates Before adaptation After adaptation Before adaptation After adaptation
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Background Subtraction 14 Tracking & Event Detection Input Data Background Subtraction Background Modeling Illumination Normalization MRF blobs Blob Extraction Mahalanobis Distance Background Models Normalized S01-S08
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Background Subtraction 15 Frame 2000 With Foreground Mask Foreground Mask Mahalanobis Distances Adapted Model Normalized - - = = MRF
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Tracking & Event Detection 16 Tracking & Event Detection Input Data Background Subtraction Background Modeling Illumination Normalization alerts blobs Kalman Tracking Meanshift Tracking Event Detection
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Idea: Focus on tracking what we care about. Loitering humans Dropped luggage that becomes dissociated from its owner Kalman tracking Constant velocity Low false positive rate Blob Tracking 17
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Loitering humans Remain in the scene for a long time Likely to create isolated tracks Dispossessed luggage Likely to create at least an isolated blob detection Detecting Humans and Luggage 18 Object Type Min. Blob Area (% of frame) Max. Blob Area (% of frame) Min. Blob Track Length humans1.5%3.0%16s luggage0.2%1.0%1 frame
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Blob tracking Yields high-quality tracks (good) Requires isolated blobs (bad) Meanshift tracker Learn a model (color histogram) from the good blob tracks Tracks through occlusions Humans Find scene entry/exit times Luggage Find drop/pickup times Associate with human owners Mean-Shift Tracking 19
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Results 20
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No events occur None detected S00 – No Defined Behavior 21
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Staged loitering 5.1s late S01 – General Loitering 1 (Easy)
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Staged loitering 1.4s late S02 – General Loitering 2 (Hard) 23
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Staged loitering 8.2s late Dropped luggage Should not trigger an alarm No alarm triggered Staged loitering man near purple-outlined woman Missed Unscripted loitering Detected S03 – Bag Swap 1 (Easy) 24
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S04 – Bag Swap 2 (Hard) 25 Stay close to each other the whole time
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S05 – Theft 1 (Easy) 26 Victim enters 19.2s late Luggage stolen 0.08s late Thief exits 0.08s late
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S06 – Theft 2 (Hard) 27 Thief Luggage (never isolated) Victims Assistant thief
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S07 – Left Luggage 1 (Easy) 28 Luggage dropped 0.12s late Owner tracked Luggage taken 0.08s late By owner
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S08 – Left Luggage 2 (Hard) 29 Owner drops: 0.2s late Owner picks up: 0.12s late
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PETS 2007 Problem description, datasets, etc. http://www.pets2007.net http://www.pets2007.net My Website The paper http://people.csail.mit.edu/dalleyg http://people.csail.mit.edu/dalleyg Websites 30
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Perguntas 31
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Illumination Normalization 32 where
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Region-of-Interest Masks 33 BG-S04 S06 S05 S07-S08
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Background Subtraction 34 Tracking & Event Detection Input Data Background Subtraction Background Modeling Illumination Normalization BG-Biased MRF FG-Biased MRF Blob Extraction fragmented blobs merged blobs Blob Extraction Mahalanobis Distance Background Models Normalized S01-S08
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Dual Background Subtraction 35 Low fragmentation But blobs merged Good for human tracking Mahalanobis Distance Map Background-Biased Blobs Foreground-Biased Blobs Sharp boundaries But fragmented blobs Good for dropped luggage detection
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Tracking & Event Detection 36 Tracking & Event Detection Input Data Background Subtraction Background Modeling Illumination Normalization alerts fragmented blobs merged blobs Kalman Tracking Meanshift Tracking Event Detection
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Luggage: it often doesn’t travel far from the owner, so we need BG-biased to avoid merging the dropped luggage blob with the owner The floor is more boring (except for specularities), so camouflaging doesn’t occur much there, relative to the vertical surfaces Easy to tell people fragments from luggage: small people fragments move They move when they’re isolated They move before and after isolation Humans With the busyness of the scene, a BG-biased MRF produces a lot of fragments and many-to-many blob matching quickly becomes impractical A FG-biased MRF avoids the fragmentation issue but merges lots of blobs We only care about loiterers Loiterers are in the scene for a long time They’re likely to be isolated from other people at least at some point in time Why Dual Trackers/Motion Blobs 37
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Oriented Ellipsoids 38
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