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DETECTING AND TRACKING TRACTOR-TRAILERS USING VIEW-BASED TEMPLATES Masters Thesis Defense by Vinay Gidla Apr 19,2010
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Introduction Object tracking: Sports analysis Games and gesture recognition Retail video mining Automobile driver assistance Traffic surveillance Volume, individual speeds, classification Lane changes, speed violations, congestions
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Feature-based vehicle tracking Beymer et al. 1997 use feature point approach with motion cues to segment vehicles using homography Kanhere et al. 2008 use features with 3D estimation using multi-level homography Feature_based.avi Feature_based.avi Drawback: These approaches track features on the vehicle, not vehicle as a whole
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Template-based tracking Model the object by 2D template of image intensities Compare search image with template image Comparison usually by discrete cross-correlation Good: Both spatial and appearance information Able to retrieve shape of the object Bad: Encode vehicle appearance from single viewpoint Do not adapt to changes in appearance of object
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Proposal Overcome the limitations of a single template by using a template sequence instead of a single template The template sequence encapsulates all of the vehicle’s perspective deformations As a starting step, aim to detect and track contours of tractor-trailers in multi-lane traffic
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Video Sequences
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Template creation Training sequence: A portion of traffic video containing a tractor- trailer Process the video frames to create a template sequence
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Training Sequence
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Training frame
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Manual contour selection
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Template creation
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Template sequence
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Algorithm Overview
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Step 1: Background subtraction
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Reference background
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Input Video Frame
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Background subtracted frame
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Step 2: Blob-Template match
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Blob-Template match
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Plot of Blob-Template match
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Step 3: Trace contour
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Results based on template-blob correlation
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Plot of misalignment
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Gradient magnitude match Reduce the misalignment by including salient features such as points of high gradient magnitude These points are located at identical spatial locations in every tractor-trailer
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Training frame
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Gradient Magnitude
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Template Gradient Magnitude
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Template Gradient Sequence
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Results based on template-frame gradient match
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Plot of misalignment
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Test sequences
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Results(Lane 3)
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Results(Lane 2)
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Level set-based tracking for automatic template generation
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Conclusion The new approach accurately traces the contours of all the tractor-trailers in the traffic video Works for multi-lane traffic Minor misalignment
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Future extensions Tracking other classes of vehicles such as passenger cars, buses etc Compact template sequence with minimal template redundancy Implement matching using level set techniques
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Thank You
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Questions & Discussion
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