Detecting and Tracking Tractor-Trailers Using View-Based Templates
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Transcript of Detecting and Tracking Tractor-Trailers Using View-Based Templates
DETECTING AND TRACKING
TRACTOR-TRAILERS USING VIEW-BASED TEMPLATES
Masters Thesis Defense byVinay GidlaApr 19,2010
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
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 homographyFeature_based.avi
• Drawback: These approaches track features on the vehicle, not vehicle as a whole
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 informationAble to retrieve shape of the object
• Bad: Encode vehicle appearance from single viewpoint
Do not adapt to changes in appearance of object
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
Video Sequences
Template creation
Training sequence:
• A portion of traffic video containing a tractor-trailer
• Process the video frames to create a template sequence
Training Sequence
Training frame
Manual contour selection
Template creation
Template sequence
Algorithm Overview
Step 1: Background subtraction
Reference background
Input Video Frame
Background subtracted frame
Step 2: Blob-Template match
Blob-Template match
Plot of Blob-Template match
Step 3: Trace contour
Results based on template-blob correlation
Plot of misalignment
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
Training frame
Gradient Magnitude
Template Gradient Magnitude
Template Gradient Sequence
Results based on template-frame gradient
match
Plot of misalignment
Test sequences
Results(Lane 3)
Results(Lane 2)
Level set-based trackingfor automatic template
generation
Conclusion
• The new approach accurately traces the contours of all the tractor-trailers in the traffic video
• Works for multi-lane traffic
• Minor misalignment
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
Thank You
Questions &
Discussion