The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

This paper presents a vision-based framework for intelligent vehicles to detect and track people riding bicycles in urban traffic environments. To deal with dramatic appearance changes of a bicycle according to different viewpoints as well as nonrigid nature of human appearance, a method is proposed which employs complementary detection and tracking algorithms. In the detection phase, we use multiple view-based detectors: frontal, rear, and right/left side view. For each view detector, a linear Support Vector Machine (SVM) is used for object classification in combination with Histograms of Oriented Gradients (HOG) which is one of the most discriminative features. Furthermore, a real-time enhancement for the detection process is implemented using the Integral Histogram method and a coarse-to-fine cascade approach. Tracking phase is performed by a multiple patch-based Lucas-Kanade tracker. We first run the Harris corner detector over the bounding box which is the result of our detector. Each of the corner points can be a good feature to track and, in consequence, becomes a template of each instance of multiple Lucas-Kanade trackers. To manage the set of patches efficiently, a novel method based on spectral clustering algorithm is proposed. Quantitative experiments have been conducted to show the effectiveness of each component of the proposed framework.

Notes

Sponsor: General Motors

Associated Project(s): Context-sensitive bicycle and pedestrian detection and tracking

Number of pages: 23

Text Reference

Hyunggi Cho, Paul Rybski, and Wende Zhang, "Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles ," tech. report CMU-RI-TR-10-11, Robotics Institute, Carnegie Mellon University, January, 2010

BibTeX Reference

@techreport{Cho_2010_6595,
   author = "Hyunggi Cho and Paul Rybski and Wende Zhang",
   title = "Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles ",
   booktitle = "",
   institution = "Robotics Institute",
   month = "January",
   year = "2010",
   number= "CMU-RI-TR-10-11",
   address= "Pittsburgh, PA",
}

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