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Published byHannah Shepherd Modified over 9 years ago
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Sean M. Ficht
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Problem Definition Previous Work Methods & Theory Results
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Track and follow specific person with a mobile robot Cluttered environments Brief occlusion Long occlusion Cooperative user
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Helper robot Carry items for a person Example: Hospital situation
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Problem Definition Previous Work Methods & Theory Results
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Person following with a mobile robot Appearance based Optical flow based Stereo vision based
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Segmentation of image Classification Detection Limitations Sidenbladh, Kragic, and Christensen; ICRA; 1999 Tarokh and Ferrari; Journal of Robotic Systems; 2003 Schlegel, Illman, Jaberg, Schuster, and Worz; British Machine Vision Conference; 2005
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Calculate optical flow Use to segment image Limitations Chivilo, Mezzaro, Sgorbissa, and Zaccaria; IROS; 2004 Piaggio, Fornaro, Piombo, Sanna, and Zaccaria; IEEE ISIC/CIRA/ISAS joint conference; 1998
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Find features Segment from background Use to track Limitations Zhichao and Birchfield; IROS; 2007
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Problem Definition Previous Work Methods & Theory Results
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Kinect Provides a depth image Provides a RGB color image Packaged solution
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Detection and Tracking Generic detector Specific appearance model Integrating particle filter Robot Control
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HOG person detector (OpenCV) HOG descriptor o Cells -> Block -> Window o 4 cells in a block o 105 blocks in a window o 64x128 window Training Dalal and Triggs, CVPR, 2005
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Gradient of the Image Binning of pixels in cells Grouping of cells into blocks Normalization
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Kernel convolution Magnitude = (g x 2 + g y 2 ) Angle = arctan(g y /g x ) Directional change in intensity
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Bins apply to each cell Nine separate bins Gradient magnitude added to bin
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Cells grouped into blocks 4 cells per block Blocks overlap one another
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HOG person detector HOG descriptor o Cells -> Block -> Window o 4 cells in a block o 105 blocks in a window o 64x128 window Training
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Support Vector Machine (SVM) classifier Binary classifier Trained on images
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Detection and Tracking Generic detector Specific appearance model Integrating particle filter Robot Control
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Color Histogram Segmentation by depth to create template
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Represents distribution of colors 10 bins for each color channel 1000 element color histogram Pixel classification 2 bin example Bin 1: 0-127 Bin 2: 128-255
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Average depth Threshold (0.3 meters) Template used to make color histogram
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Detection and Tracking Generic detector Specific appearance model Integrating particle filter Robot Control
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System State Motion model Observation model Expected state Resample
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Hybrid state space X and Y in image coordinates Scaled according to depth Z in depth coordinates
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Detection and Tracking Generic detector Specific appearance model Integrating particle filter Robot Control
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Input: tracking information from tracking algorithm Uses tracking information to make movement decisions Executes movement and returns to tracking algorithm
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Problem Definition Previous Work Methods & Theory Results
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No occlusion Other people present (different depth) Other people present (similar depth) Pose change Brief occlusion Long occlusion
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Initial template
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Initial template Non-occluded target Occluded target
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Average between 73% and 74%
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Problem Follow a person in different scenarios System RGB-D sensor Generic detector Specific appearance model Particle filter Robot control architecture Performance Performed in three separate test scenarios Rapid side to side target motion trade-off Large target scale changes
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Train a new HOG detector to handle scale issues Using more particles KLT features for trajectory histories Adaptive appearance model
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